Comparison of a new portable digital meniscometer and optical coherence tomography in tear meniscus radius measurement
Bibliographic record
Abstract
PURPOSE: Non-invasive measurement of tear meniscus radius (TMR) is useful in the assessment of tear volume for dry eye diagnosis. This study investigates the agreement between a new, portable, slit-lamp mounted, digital meniscometer (PDM) and optical coherence tomography (OCT) in the measurement of human TMR. METHODS: Images of the tear meniscus at the centre of the lower lid of 30 normal subjects (8M, 22F; mean age 27.5 SD ± 9.6 years) were taken using the PDM and the OCT. On the PDM and OCT images, TMR was measured using imagej 1.46b software. The meniscus on the OCT images was subdivided vertically into three equal sections and the radius calculated for each: bottom (BTMR), centre (CTMR) and top (TTMR). The relationship between PDM and OCT measurements was analysed using Spearman's rank coefficient, and differences between PDM and OCT subsection measurements were evaluated using Bland-Altman plots. RESULTS: Tear meniscus radius measured with the PDM (0.25 ± 0.06 mm) and OCT (0.29 ± 0.09 mm) was significantly correlated (r = 0.675; p < 0.001). The mean differences between TMR using the PDM and the subsections from OCT showed that TMR measured with PDM was greater for BTMR (0.07 mm; CI 0.05-0.10; p < 0.001), similar for CTMR (-0.01 mm; CI -0.04 to 0.02; p = 0.636) and steeper for TTMR (-0.07 mm; CI -0.10 to -0.04; p < 0.001). CONCLUSIONS: Portable digital meniscometer and OCT measurements of the TMR are significantly correlated, suggesting that the new PDM is a useful surrogate for OCT in this respect. The PDM appears to measure the radius of the central section of the tear meniscus.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".