Associations with Meibomian Gland Atrophy in Daily Contact Lens Wearers
Bibliographic record
Abstract
PURPOSE: To determine associations for contact lenses (CLs) and meibomian gland atrophy in a matched-pair study. METHODS: Contact lens wearers (case) and age- and sex-matched non-contact lens (NCL) wearers with no history of CL use (control) were recruited for a multicenter study. All subjects were administered the Ocular Surface Disease Index questionnaire and a comprehensive battery of clinical tests (e.g., tear breakup time, bulbar and limbal redness, meibography, etc.) were performed. Upper and lower eyelid meibomian gland atrophy were graded with both digital meibography (percent gland atrophy) and visual meiboscore methods. Conditional logistic regression analyses were then used to determine relationships among CL use, meibomian gland atrophy, and ocular surface signs and symptoms. RESULTS: A total of 70 matched pairs were analyzed. The mean (± SD) age of the CL group was 30.6 (± 12.4) years, and that of the NCL group was 30.1 (± 12.2) years. The subjects were 63% female. The association between CL wear and meiboscore was not significant univariately, but the best-fitting multivariate regression model showed that higher meiboscores were associated with being a CL wearer (odds ratio [OR], 2.45) in a model that included eyelid margin erythema (OR, 0.25) and lissamine green staining (OR, 1.25). Percent gland atrophy was not associated with CL wear in regression analysis (p = 0.31). CONCLUSIONS: This study determined inconclusive associations with CLs and meibomian gland atrophy. This study also provided a comprehensive assessment of differences between CL and NCL wearers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".