A Comparison of Dry Eye Diagnostic Tests Between Symptomatic and Asymptomatic Age-Matched Females
Bibliographic record
Abstract
PURPOSE: To quantify the strength of association of dry eye (DE) symptoms to DE diagnostic tests in age-matched females. METHODS: Twenty females with DE symptoms (Ocular Surface Disease Index, OSDI, ≥13) were age-matched with 20 females without DE symptoms (OSDI<13) in this cross-sectional study. Noninvasive tear breakup time (NIBUT), ocular staining, meibum quality, number of obstructed meibomian glands (MGs), lid wiper epitheliopathy, Marx line placement, eyelid margin score, Schirmer test, meibography, and visual acuity were assessed in both groups. RESULTS: Twenty participant pairs completed the study. The age (median/interquartile range [IQR]) of the symptomatic group was 60/15 and the asymptomatic group was 62/15. The tests (median/IQR, P value) that were significantly different between the symptomatic group and the asymptomatic group were OSDI (35.4/35.4 vs. 3.1/6.7, P<0.01), NIBUT (2.1/0.7 sec vs. 3.0/3.0 sec, P=0.01), meibum quality (3.0/0.0 grade units vs. 2.0/1.0 grade units, P<0.01), number of obstructed MGs (7.0/2.0 glands vs. 5.0/4.8 glands, P<0.01), and ocular staining (5.5/3.8 grade units vs. 0.5/1.0 grade units, P<0.01). The tests (area under curve, [AUC], odds ratio [OR]) that were most strongly associated with DE symptoms were ocular staining (0.93, 5.0), number of obstructed MGs (0.79, 2.6), meibum quality (0.76, 2.4), and NIBUT (0.74, 3.2) (all P<0.05). There was no significant difference between the two groups for the other DE tests (all P>0.05), and similarly, no significant association to DE symptoms (all P>0.05). CONCLUSION: The diagnostic tests most strongly associated with DE symptoms in older women were ocular staining, meibum quality, number of obstructed MGs, and tear film stability.
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 | 0.003 |
| 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) | 0.002 | 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".