ArticleOphthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists)2026
Analytical Performance, Spatial Dynamics and Clinically Meaningful Change Thresholds for Automated Non-invasive Tear Film Assessment Using the Oculus Keratograph 5M.
Article in Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeNon-invasive tear film metrics from automated topographers are used increasingly, yet device-specific analytical performance remains incompletely characterised. This study quantified the analytical performance of the Oculus Keratograph 5M for non-invasive break-up time (NIKBUT) and tear meniscus height (KTMH), evaluated agreement with conventional methods and characterised the spatial dynamics of tear film break-up.
methodsThirty-five participants (18 symptomatic, 17 asymptomatic) attended three visits on consecutive days. NIKBUT (first and average), KTMH, fluorescein break-up time (FBUT) and slit-lamp tear meniscus height (TMH) were each measured three times per eye per visit. Precision (coefficient of variation, CV), reliability (intraclass correlation, ICC 3,1), standard error of measurement (SEM) and minimum detectable change at 95% confidence (MDC₉₅) were calculated. Method agreement was assessed using random-effects Bland-Altman analysis. Spatial distribution of break-up events was analysed by the corneal zone.
resultsKTMH demonstrated excellent precision (CV = 8.8%) and moderate-to-good reliability (ICC = 0.73), with an MDC₉₅ of 0.173 mm. NIKBUT showed poor precision (CV = 53.6% for First, 42.8% for Average) and symptom-dependent reliability. FBUT required a change exceeding 9.28 s to surpass its MDC₉₅. Bland-Altman analysis confirmed systematic bias between NIKBUT and FBUT, with limits of agreement spanning more than 19 s in width and significant proportional bias. Spatial analysis revealed that NIKBUT break-up occurred predominantly paracentrally (53-63%), while FBUT events concentrated centrally (86-97%), indicating the methods captured fundamentally different tear film phenomena. Intra-subject repeatability of break-up location was poor (Krippendorff's α = 0.12-0.31).
conclusionsKTMH is the most analytically robust Keratograph metric, suitable for longitudinal monitoring when changes exceed its MDC₉₅ of 0.173 mm. NIKBUT shows poor precision; only large changes exceeded noise. Spatial analysis confirms that NIKBUT and FBUT interrogate distinct biophysical processes-these methods are not interchangeable. These benchmarks should inform clinical interpretation and study design.
Indexed as
Identifiers
42319728What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.