Tear Meniscus Height Determination Using the OCT2 and the RTVue‐100
Bibliographic record
Abstract
PURPOSE: To compare anterior segment spectral-domain optical coherence tomography (SOCT) tear meniscus height (TMH) measures with those from the more commonly used time-domain OCT (TOCT). METHODS: The right eye of 50 healthy subjects had images of their TMH captured with TOCT (OCT2) and SOCT (RTVue-100). Data were acquired using two different anterior segment lenses, the cornea/anterior module-short (CAM-S) and long (CAM-L), on the SOCT. Images were then analyzed for differences in their derived TMH. RESULTS: The average TMH for TOCT was 0.280 ± 0.139 mm, whereas the mean TMH measured using the SOCT was 0.354 ± 0.163 mm and 0.345 ± 0.167 mm for the CAM-S and CAM-L, respectively. There was a significant difference (p < 0.001) when comparing TOCT with either of the SOCT lenses. There was no statistically significant difference between the CAM-L and CAM-S (p = 1.0). Bland-Altman analysis showed poor agreement between TOCT and SOCT (95% limits of agreement -0.138 to + 0.285 mm for the CAM-S and -0.185 to +0.315 mm for the CAM-L). CONCLUSIONS: The RTVue-100 produces TMH measurements that are significantly higher than OCT2 in a normal patient population. However, the RTVue-100 showed a number of other advantages over the OCT2 in the measurement and analysis of images. Future work needs to determine the causative factors behind the observed differences.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".