September 2004
Volume 45, Issue 9
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Visual Psychophysics and Physiological Optics  |   September 2004
Flicker Perimetry Losses in Age-Related Macular Degeneration
Author Affiliations
  • Joanna A. Phipps
    From the Department of Optometry and Vision Sciences, University of Melbourne, Victoria, Australia; and the
  • Trung M. Dang
    From the Department of Optometry and Vision Sciences, University of Melbourne, Victoria, Australia; and the
  • Algis J. Vingrys
    From the Department of Optometry and Vision Sciences, University of Melbourne, Victoria, Australia; and the
  • Robyn H. Guymer
    Centre for Eye Research Australia, University of Melbourne, Victoria, Australia.
Investigative Ophthalmology & Visual Science September 2004, Vol.45, 3355-3360. doi:10.1167/iovs.04-0253
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      Joanna A. Phipps, Trung M. Dang, Algis J. Vingrys, Robyn H. Guymer; Flicker Perimetry Losses in Age-Related Macular Degeneration. Invest. Ophthalmol. Vis. Sci. 2004;45(9):3355-3360. doi: 10.1167/iovs.04-0253.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

purpose. To compare static and flicker perimetry outcomes in patients with early age-related macular degeneration (AMD).

methods. Perimetry was performed in the central visual field of one eye of each of 25 patients with good visual acuity (>6/12) and early AMD using static and flickering targets. These results were compared with data obtained from a single eye of 34 age-matched control subjects, 33 of whom were retested at 1 to 3 months after their initial visits.

results. In all cases, patients with early AMD had greater mean defects for flickering than static targets, returning a significantly larger group average in response to flicker (4.3 ± 0.6 dB) than to static (1.8 ± 0.6 dB; P < 0.005). Greater pattern defect losses were also present in AMD-affected eyes with flicker compared with static perimetry (P < 0.02). These give a higher diagnostic sensitivity for flicker (68% vs. 42%, P < 0.05) at 90% specificity. Sensitivity can be increased to 84% ± 6% (specificity 92% ± 4%) if the criterion for failure is a more than 10-dB loss in the foveal region (1°–3°).

conclusions. Flickering targets expose foveal deficits in early AMD better than do static targets. Flicker perimetry is an easy, short procedure that may be useful for monitoring the progression of AMD.

Age-related macular degeneration (AMD) is a disorder of the macula that causes severe vision loss in the elderly. 1 The early stages of AMD comprise pigmentary changes and drusen development, which contrast with the more severe forms that involve development of geographic atrophy or neovascular membranes. 2 It is this early form of AMD that is important, because it heralds the loss of a normal retinal status and indicates a greater risk of central vision loss. During this phase, although visual acuity can be normal, other visual functions are affected. In particular, losses of sensitivity for flickering and chromatic stimuli have been reported, 3 4 5 6 7 8 9 10 as has a slowed adaptive capacity for both rods and cones. 7 10 11 12 It is possible that a visual function test may be a better way to assess early AMD and predict risk of progression to severe vision loss than the currently available clinical grading scheme. The purpose of this study was to determine the role flicker perimetry may have in this regard. 
We propose that because outer retinal metabolism is compromised in early AMD, a flickering stimulus should be less readily detectable than a static stimulus, because of the increased metabolic requirement needed to detect flicker. 13 14 15 This metabolic challenge cannot be met as readily by eyes with AMD changes, so a flickering stimulus should detect functional changes earlier than static stimuli. In this investigation, we tested the hypothesis that flicker perimetry is more sensitive to detecting early AMD than is static automated perimetry. 
A flickering stimulus can be generated in several ways, by either modulating a stimulus about a mean background level, or concurrent with a light increment, as in a chopped stimulus. The former has been called mean-modulated flicker, and the latter is known as pedestal flicker. 16 17 With the latter, the low-frequency luminous pedestal, created for the stimulus duration, is known to affect detection threshold 18 by invoking local adaptation and contrast-dependent masking effects 16 19 or rod-cone and cone-cone interactions from surrounding regions. 19 20 21 22 23 Mean-modulated flicker has been shown to be abnormal in patients with AMD. 4 5 Given that pedestal flicker also invokes other interactions it is possible that use of such stimuli would be a better means for exposing early AMD. The following experiment examines this possibility. 
Materials and Methods
Our approach adopted the experimental design and statistical recommendations for evaluating ophthalmic diagnostic tests 24 as outlined in the following sections. 
Subjects
A single eye of 25 individuals with early AMD (69.4 ± 6.2 years) and 34 age-similar control subjects (68.9 ± 5.4 years) were tested. In all cases, the study eye had the better acuity, but in those cases where acuity was the same, a random assignment was made. Inclusion criteria for both control and AMD subjects required a visual acuity of 6/12 or better, age-normal lens changes, normal intraocular pressure (<21 mm Hg), no medications known to affect vision, and no history of ocular or systemic disease except AMD (described later) and controlled hypertension. 
All subjects had a dilated fundoscopic examination and people who had a normal fundus (fewer than five hard drusen and no focal hyperpigmentation) and met our inclusion criteria were asked to participate as controls. Subjects with drusen and pigmentary changes had fundus photography and fluorescein angiography performed. Fundus photographs were scored according to the Wisconsin grading scale 25 26 by two experienced graders, as detailed elsewhere. 10 Those who had greater than five soft drusen (>63 μm) with or without pigment changes or had an end-stage choroidal neovascular lesion in their fellow eye were included in our AMD group. This group is considered as being at high risk of development of the late complications of AMD in the study eye. 27 Seven of our patients with AMD had fellow eyes with an end-stage neovascular lesion and three of these eyes had minimal AMD involvement in the study eye. 
All subjects provided written informed consent to participate, as required by our institutional ethics committee and as detailed in the Declaration of Helsinki. The performance of a subset of patients has been reported elsewhere on other vision tests. 10  
Perimetric Methods
Subjects were tested using an automated perimeter (model M-700; Medmont Pty Ltd., Camberwell, Victoria, Australia). The M-700 is a bowl perimeter that uses light-emitting diodes (LEDs) as stimuli (λmax = 565 nm). 28 29 The bowl has a background luminance of 3.2 cd/m2 (CIE 1931 x:0.53, y:0.42) and a maximum spot luminance of 320 cd/m2. The LEDs subtend 0.43° (Goldmann size III) and are arranged concentrically at various eccentricities from 1° to 50°. It has several test patterns but in this study we used the 48-point macula test, locating points at 1°, 3°, 6°, and 10°. 
The test uses a ZEST fast-Baysean threshold logic 30 and takes some 4 to 7 minutes to complete. Static stimuli or flickering stimuli were presented with durations of 200 ms (static) or 800 ms (flicker). Subjects were verbally instructed on each test and allowed a 2-minute practice trial during which errors were pointed out and positive reinforcement given for correct responses. Thereafter, the perimetric testing was performed without any feed back from the operator with the static test given first. Controls were retested after a 1- to 3-month period to establish variability. One normal observer failed to attend on retesting reducing our control sample to 33 for the retest analysis. 
Fixation was checked by a blind-spot monitor and false-positive and -negative stimuli were presented randomly during testing. Patients who gave more than 20% false-positive or false-negative responses, or more than 20% fixation losses were reinstructed or realigned and retested. 31 32  
Flicker thresholds were determined using the autoflicker test. This test varies the temporal frequency of the stimulus with eccentricity to enhance the dynamic range of the test. 33 In the autoflicker mode, thresholds are obtained at 1° and 3° with stimuli flickering at 18 Hz, 16 Hz at 6°, and 12 Hz at 10°. Subjects were asked to respond only when the stimulus appeared flickering. Static (non-flickering) false-positive stimuli were presented near threshold to monitor the subject’s compliance with the response instruction. An audible beep followed a false-positive response, and verbal feed back was given on response error during an initial two minutes of demonstration used to familiarize the subject with the task. Vingrys et al. 33 have shown that such criterion setting can successfully isolate flicker sensitive mechanisms and McKendrick et al. 29 and Landers et al. 34 show that this testing modality can be successfully applied to detect flicker losses in clinical populations. 
Statistical Analysis
Test outcomes were considered by calculating: a mean defect (MD), a pattern defect (PD), the number of abnormal points in the field and the retest performance of normal controls. As the normal database was limited for some perimetric indices in elderly populations, we chose to develop our own normative data. The initial test of our 34 controls formed the age-matched database for each location and indices were developed using this database. 
Comparisons between the normal and AMD groups were performed with t-tests. Proportions were evaluated for significance using a χ2 statistic. Correlations were evaluated using a Spearman rank order correlation. A receiver operator characteristic (ROC) yielded test sensitivity and specificity and their 95% confidence limits (CL) as recommended by Harper and Reeves. 24  
Summary Indices.
The MD was determined as the average of the point-wise deviations from the normal database over all locations, with a positive value indicating loss. At each location we also calculated the 5% and 95% confidence limits (90% confidence interval) for our controls. Points that were abnormally low and exceeded the 5% confidence limit were averaged to give the PD. 
As our subjects were matched for age with most within the same decade (AMD, 69.4 ± 6.2 years; control subjects 68.9 ± 5.4 years), we did not adjust our summary statistics for age. In addition, we do not believe that adjusting for age would have yielded stronger effects, as ageing has its greatest effect in peripheral locations. 35  
The number of statistically abnormal points (beyond the 5% confidence limit) was also chosen as a method of assessing field loss, because it is commonly used in a clinical setting when making qualitative judgments and has been successfully applied to compare visual field results. 29 Binomial statistics can be used to predict the number of points that can be expected to lie beyond the 5% limit by chance and still belong to a normal field. Assuming points are independent, then the probability that n points of a total of N tested (N = 48 in our case) being found beyond the 5% confidence limit is returned by  
\[P(n)\ {=}\ _{N}C_{n}\ {\cdot}\ {\alpha}^{n}\ {\cdot}\ (1\ {-}\ {\alpha})^{N{-}n}\]
where α is the probability that an individual point will fall beyond the confidence limit and still belong to the normal population, n is the number of abnormal points, N is the total number of test points in the field and C is the number of combinations comprising n points, for sample size N, in which order is not important. Equation 2 gives the cumulative probability that n or more points fall outside the confidence limits and still belong to a normal population.  
\[P(\mathrm{cum})\ {=}\ 1\ {-}\ {{\sum}_{n}^{N}}\ P(n)\]
Solving Equation 2 shows that a visual field can be considered abnormal (P < 0.02) if four or more points lie beyond the 5% limit. 
Retest Reliability.
Retest variability was considered for both static and flicker perimetry by establishing variability as a function of initial sensitivity. 36 37 Variability was determined in our normal group only, as patients with AMD are expected to change over time. 
Results
Figure 1 shows the thresholds of one patient with AMD on both static and flicker perimetry typical of this group. This subject had a small central depression on static perimetry and a larger and deeper depression on flicker perimetry. That different points show losses in static and flicker perimetry suggests that these test modalities identify different causes for the loss, an issue that is considered later. In Figure 1 , and in subsequent figures, thresholds values are shown in the decibel units returned by the perimeter. 
Mean and Pattern Defect
On average, the AMD group (Fig. 2 , filled symbols) had a significantly greater MD (Fig. 2 , top) for both static (1.8 ± 0.6; P < 0.01) and flicker perimetry (4.3 ± 0.6; P < 0.001) than the control group, although many AMD observers returned values within the normal domain. A similar outcome was found for the PD on static perimetry testing (Fig. 2 , bottom) with many of the AMD group having zero scores. However, for flicker perimetry, only 1 AMD subject returned a zero PD compared with 20 control subjects, confirming that most patients with AMD have flicker losses. If we consider only those subjects who had nonzero PDs, the AMD group averages for both static (5.2 ± 0.4, P < 0.02) and flicker (7.6 ± 0.5, P < 0.001) are significantly greater than control averages (static 3.5 ± 0.5; flicker 3.9 ± 0.7). The seven eyes that had end-stage lesions in the fellow eye did not perform significantly differently from the other AMD eyes. 
Relationship between Static and Flicker Indices
Figure 3 plots the relationship between static and flicker mean and PDs for both groups of participants. The MD correlation (Fig. 3 , top) was significant (P < 0.0001) in both control (open symbols, Rs = 0.70; 95% confidence limits [CL]0.47, 0.84) and AMD subjects (filled symbols, Rs = 0.82; 95% CL: 0.62, 0.92), and in the AMD group all flicker MDs were greater than were their static counterparts (shaded region). The correlation for the PD (Fig. 3 , bottom) was not significant in control subjects (Rs = 0.24, P = 0.18; 95 % CL: −0.12, 0.54) but it was for the AMD group (Rs = 0.52, P = 0.01; 95% CL: 0.13, 0.77). These data confirm our impression gained from the single observer (Fig. 1) that flicker produces deeper losses in early AMD. Figure 3 also shows that the flicker loss was greater in the majority of AMD eyes (21 [84%] of 25 AMD eyes versus 12 [35%] of 34 control subjects χ2=6.15, P < 0.025) and in no case did an AMD eye give a static loss with a zero-flicker PD. 
Number of Abnormal Points
Figure 4 plots the number of abnormal (P < 0.05) points for both groups of observers for flicker perimetry only. The dashed horizontal line indicates less than four abnormal points at the 5% limit by chance (see the Methods section). This finds 13 (52%) of 25 AMD eyes (Fig. 4 , filled bars) returned a significant number of abnormal thresholds compared with 4 (12%) of 34 eyes (Fig. 4 , shaded bars) in control subjects (χ2 = 11.37, P < 0.001). A smaller number of AMD and control eyes were abnormal with static perimetry (7 [28%] of 25, vs. 3 [9%] of 34: χ2 = 3.76, P > 0.05, data not shown). 
Receiver Operator Characteristics
The receiver operator characteristics (ROC) analysis for PDs returned from static (Fig. 5 , open symbols) and flicker (Fig. 5 , filled symbols) testing is plotted in Figure 5 , and confirms the better diagnostic capacity of flicker perimetry. The best performance (81% ± 9% correct diagnoses) was obtained with criteria of 3 and 6 dB, although 3 dB returned a greater number of false positives. At the specificity of 90%, flicker perimetry (68%, 95% CL: 50%, −86%) yields a significantly higher sensitivity (P < 0.05) than static perimetry (42%). Sensitivity for flicker loss is greater if deficits within the foveal (1°–3°) rings alone are considered. Using the criterion of a flicker loss of ≥10 dB at any location in this region gives a specificity of 92% (95% CL: 83%, 100%) and a sensitivity of 84% (95% CL: 70%, 98%) for AMD. 
Test-Retest Reliability
Given that flicker testing provides greater clinical information, it is important to consider its reproducibility over time. In the 33 normal subjects who returned for retest, the MD did not change significantly at retest from its initial value (static: +0.73 ± 1.07 dB; flicker: +1.07 ± 1.04 dB: P > 0.05 both, df = 32). Figure 6 shows the test-retest variability in both flicker and static thresholds, giving the mean and upper (5%) and lower (95%) confidence limits for retest threshold as a function of initial test sensitivity. To yield sample sizes that provide meaningful confidence limits, the data were grouped into 3-dB bins, with data sets comprising more than 20 samples used to calculate the confidence limits. This shows that a patient with an initial threshold of 10 dB will return anywhere from 0 to 26 dB on retest. Consistent with the literature, both forms of perimetry show similar and large increases in variability as thresholds decrease. 36 37 Although the sensitivity ranges of the static and flicker tests are different (wider on the x-axis), that the flicker data show similar variability to static thresholds over the mean sensitivities common to both tests (10–28 dB) suggests that flicker thresholds are no more variable than are static thresholds at equivalent decibel values. 
Discussion
We found that our AMD subjects had a significantly greater MD in static perimetry testing than control subjects, consistent with studies reported in the literature. 38 39 40 41 The interesting finding from our study is that flicker perimetry produced larger and deeper defects than did static perimetry, with 52% of AMD subjects having large abnormalities to flickering targets and 84% having some localized depressions (≥10 dB) in the foveal region (1°–3°). This raises the prospect that a selective loss for flickering targets may be present earlier in the disease process than for static targets, and, as such, it may provide an earlier indicator of risk of progression to end-stage disease, as has been reported previously. 5 42  
We propose the reason that flicker thresholds are more adversely affected in AMD than are static thresholds is because the flickering target stresses retinal capacity more than does the static target. In early AMD, the photoreceptors are close to their functional limits, and the increase in local metabolic demand and retinal blood flow required by flicker 13 14 15 cannot readily be supplied by the AMD fundus. This concept supports previous studies that have shown that losses in flicker sensitivity precede the onset of neovascular changes in a group of AMD subjects. 5 42 An alternative explanation for the flicker loss is that a spatiotemporal (magnocellular) pathway is preferentially affected in AMD. This theory is supported by the midtemporal frequency losses found in AMD. 4 10 However, AMD does not have the loss of contrast at low spatial frequencies that would be associated with such losses, 10 43 44 suggesting that changes involve the outer retina consistent with the ultrastructural changes. 45 It should also be noted that the different durations of the static and flickering stimuli used in these tests (200 vs. 800 ms) may have affected our outcomes. However, we feel this is unlikely, as any change due to stimulus duration would be biased toward decreased sensitivity to the shorter-duration static stimuli, contrary to our findings. 
Pedestal flicker, as used on the perimeter (Medmont), is known to alter local adaptation as well as mediate flicker detection. 19 As such, the large loss of flicker sensitivity found in our group is consistent with the adaptational abnormalities reported previously in patients with AMD. 10 12 Moreover, as flicker thresholds are relatively robust to the effects of blur and preretinal absorption 33 46 47 and they show similar levels of variability to static thresholds (Fig. 6) , it is reasonable to suggest that pedestal flicker perimetry would be more useful in monitoring subjects with early signs of AMD than would be static perimetry. 
Flicker perimetry provides an easy to perform and rapid test (4–7 minutes) that may become a useful way for detecting and monitoring the progression of AMD. Flicker perimetry has already been shown to be useful in the detection of migraine suffers 29 and to correlate highly with short-wavelength automated perimetry (SWAP) in glaucoma patients. 34 Because current clinical measurements of the central visual field in AMD are largely restricted to Amsler grid testing, flicker perimetry provides additional information on the status of the central visual field in AMD, as well as being an indicator of local adaptation and temporal sensitivity changes. Given the preliminary findings that flicker may be able to predict the development of neovascular complications 5 42 it is possible that flicker perimetry can be a sensitive predictor of disease progression in AMD. Large prospective studies in patients with AMD are needed to determine whether this potential can be realized. 
 
Figure 1.
 
Representative thresholds (dB) for one AMD subject obtained with (A) static perimetry and (B) flicker perimetry. Shaded data: those points that were significantly removed from control values, as indicated by the key.
Figure 1.
 
Representative thresholds (dB) for one AMD subject obtained with (A) static perimetry and (B) flicker perimetry. Shaded data: those points that were significantly removed from control values, as indicated by the key.
Figure 2.
 
Summary indices for the visual field thresholds of our control (open symbols) and AMD (filled symbols) groups. Top: MD, bottom: PD. Outcomes are shown for (left) static and (right) flicker perimetry. Horizontal bars: group means, with the PD being a trimmed mean based on the set of nonzero values.
Figure 2.
 
Summary indices for the visual field thresholds of our control (open symbols) and AMD (filled symbols) groups. Top: MD, bottom: PD. Outcomes are shown for (left) static and (right) flicker perimetry. Horizontal bars: group means, with the PD being a trimmed mean based on the set of nonzero values.
Figure 3.
 
The relationship between flicker and static indices for MD (top) and PD (bottom). Filled symbols: AMD subjects; open symbols: control subjects. Diagonal: perfect concordance; shaded area: points where flicker losses were greater than static defects. Correlations for MD (top) are 0.85 for AMD and 0.76 for control subjects (P < 0.0001 both) and for PD (bottom) are 0.52 for AMD (P < 0.01) and 0.24 for control subjects (P > 0.05).
Figure 3.
 
The relationship between flicker and static indices for MD (top) and PD (bottom). Filled symbols: AMD subjects; open symbols: control subjects. Diagonal: perfect concordance; shaded area: points where flicker losses were greater than static defects. Correlations for MD (top) are 0.85 for AMD and 0.76 for control subjects (P < 0.0001 both) and for PD (bottom) are 0.52 for AMD (P < 0.01) and 0.24 for control subjects (P > 0.05).
Figure 4.
 
The number of abnormal points in AMD (▪) and control subjects ( Image not available ) that lie beyond the upper confidence limit (5%) of control performance for flicker perimetry. Horizontal line: the number of abnormal points that could be expected by chance in a normal population (P < 0.02). (★) Subject represented in Figure 1 .
Figure 4.
 
The number of abnormal points in AMD (▪) and control subjects ( Image not available ) that lie beyond the upper confidence limit (5%) of control performance for flicker perimetry. Horizontal line: the number of abnormal points that could be expected by chance in a normal population (P < 0.02). (★) Subject represented in Figure 1 .
Figure 5.
 
An ROC analysis for static (open symbols) and flicker (filled symbols) perimetry at different PD fail criteria (numerals, decibels).
Figure 5.
 
An ROC analysis for static (open symbols) and flicker (filled symbols) perimetry at different PD fail criteria (numerals, decibels).
Figure 6.
 
Retest threshold confidence limits (5% top lines and 95% bottom lines) in the control group (n = 33) as a function of initial threshold value (decibels) for static (dotted lines) and flicker (solid lines) perimetry, calculated using 3-dB bins. The means are denoted by symbols (open, static; filled, flicker).
Figure 6.
 
Retest threshold confidence limits (5% top lines and 95% bottom lines) in the control group (n = 33) as a function of initial threshold value (decibels) for static (dotted lines) and flicker (solid lines) perimetry, calculated using 3-dB bins. The means are denoted by symbols (open, static; filled, flicker).
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Figure 1.
 
Representative thresholds (dB) for one AMD subject obtained with (A) static perimetry and (B) flicker perimetry. Shaded data: those points that were significantly removed from control values, as indicated by the key.
Figure 1.
 
Representative thresholds (dB) for one AMD subject obtained with (A) static perimetry and (B) flicker perimetry. Shaded data: those points that were significantly removed from control values, as indicated by the key.
Figure 2.
 
Summary indices for the visual field thresholds of our control (open symbols) and AMD (filled symbols) groups. Top: MD, bottom: PD. Outcomes are shown for (left) static and (right) flicker perimetry. Horizontal bars: group means, with the PD being a trimmed mean based on the set of nonzero values.
Figure 2.
 
Summary indices for the visual field thresholds of our control (open symbols) and AMD (filled symbols) groups. Top: MD, bottom: PD. Outcomes are shown for (left) static and (right) flicker perimetry. Horizontal bars: group means, with the PD being a trimmed mean based on the set of nonzero values.
Figure 3.
 
The relationship between flicker and static indices for MD (top) and PD (bottom). Filled symbols: AMD subjects; open symbols: control subjects. Diagonal: perfect concordance; shaded area: points where flicker losses were greater than static defects. Correlations for MD (top) are 0.85 for AMD and 0.76 for control subjects (P < 0.0001 both) and for PD (bottom) are 0.52 for AMD (P < 0.01) and 0.24 for control subjects (P > 0.05).
Figure 3.
 
The relationship between flicker and static indices for MD (top) and PD (bottom). Filled symbols: AMD subjects; open symbols: control subjects. Diagonal: perfect concordance; shaded area: points where flicker losses were greater than static defects. Correlations for MD (top) are 0.85 for AMD and 0.76 for control subjects (P < 0.0001 both) and for PD (bottom) are 0.52 for AMD (P < 0.01) and 0.24 for control subjects (P > 0.05).
Figure 4.
 
The number of abnormal points in AMD (▪) and control subjects ( Image not available ) that lie beyond the upper confidence limit (5%) of control performance for flicker perimetry. Horizontal line: the number of abnormal points that could be expected by chance in a normal population (P < 0.02). (★) Subject represented in Figure 1 .
Figure 4.
 
The number of abnormal points in AMD (▪) and control subjects ( Image not available ) that lie beyond the upper confidence limit (5%) of control performance for flicker perimetry. Horizontal line: the number of abnormal points that could be expected by chance in a normal population (P < 0.02). (★) Subject represented in Figure 1 .
Figure 5.
 
An ROC analysis for static (open symbols) and flicker (filled symbols) perimetry at different PD fail criteria (numerals, decibels).
Figure 5.
 
An ROC analysis for static (open symbols) and flicker (filled symbols) perimetry at different PD fail criteria (numerals, decibels).
Figure 6.
 
Retest threshold confidence limits (5% top lines and 95% bottom lines) in the control group (n = 33) as a function of initial threshold value (decibels) for static (dotted lines) and flicker (solid lines) perimetry, calculated using 3-dB bins. The means are denoted by symbols (open, static; filled, flicker).
Figure 6.
 
Retest threshold confidence limits (5% top lines and 95% bottom lines) in the control group (n = 33) as a function of initial threshold value (decibels) for static (dotted lines) and flicker (solid lines) perimetry, calculated using 3-dB bins. The means are denoted by symbols (open, static; filled, flicker).
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