The Relationship between Tear Meniscus Regularity and Conjunctival Folds
Bibliographic record
Abstract
PURPOSE: To investigate the capability of a new portable digital meniscometer (PDM) to measure tear meniscus radius (TMR) and tear meniscus height (TMH) at different locations along the lower lid and to evaluate relationships between tear meniscus regularity and the degree of lid-parallel conjunctival folds (LIPCOFs). METHODS: Using the PDM, the TMR and TMH of 42 subjects were measured at three locations along the lower lid of one eye: central, perpendicularly below the pupil center (TMR-C, TMH-C), and temporal (TMR-T, TMH-T) and nasal (TMR-N, TMH-N), perpendicularly below the limbus. Nasal and temporal LIPCOF grades were recorded. Correlations between the measurements were analyzed using the Pearson coefficient (or Spearman rank in nonparametric data), and the differences were evaluated by paired t tests or analysis of variance and post hoc Fisher least significant difference test. RESULTS: Temporal TMR was 0.041 mm flatter (p = 0.002) and TMH-T was 0.063 mm higher (p < 0.001), whereas TMR-N was 0.026 mm flatter (p = 0.038) and TMH-N was 0.046 mm higher (p < 0.001) than TMR-C and TMH-C. Temporal LIPCOF grades were significantly correlated to temporal alterations in TMH (r = 0.590; p < 0.001) and TMR (r = 0.530; p < 0.001), and nasal LIPCOF grades were significantly correlated to nasal alterations in TMH (r = 0.492; p = 0.001) and TMR (r = 0.350; p = 0.023). CONCLUSIONS: The PDM is able to noninvasively detect significant differences in TMR and TMH along the lower lid. The flatter TMR and higher TMH at the nasal and temporal locations are associated with increased LIPCOF. Because increased LIPCOF scores may affect tear film disruption along the lower lid, measuring TMR and TMH at the central position below the pupil may provide the best intersubject reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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".