Variability of the Analysis of the Tear Meniscus Height by Optical Coherence Tomography
Bibliographic record
Abstract
PURPOSE: Tear meniscus height (TMH) is an established parameter indicative of tear film volume and has recently been determined using an optical coherence tomographer (OCT). The purpose of this study was to evaluate the inter and intra observer variability in TMH assessment using OCT. METHODS: Ten subjects (6 M, 4 F; aged 32.5 +/- 6.4 years) had 10 consecutive scans taken of their inferior central tear meniscus (5 scans originating at 90 degrees and 5 origination at 270 degrees) using the OCT2 (Humphrey-Zeiss). Images were analyzed by two observers using custom software on three separate occasions. Following a training session among observers, the images were reevaluated to assess differences in variability. Data were analyzed for differences within and across examiners, for the effect of examiner training and between scan directions. RESULTS: The mean TMH and tear volume collapsed across subjects were between 0.24 and 0.25 mm and 25 to 27 nL/mm, respectively. No difference was noted within observers. An interobserver mean volume difference (p = 0.044) was present but was eliminated post training (p = 0.167). Variability was less with scans originating at 90 degrees. CONCLUSIONS: The values of the TMH and tear volume are similar to those reported in the literature. Due to the interobserver differences observed, a training session between examiners may prove to be valuable, especially in a large or multicenter study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".