Soft Contact Lens‐Related Dryness with and without Clinical Signs
Bibliographic record
Abstract
PURPOSE: To report demographics, wearing patterns, and symptoms from soft contact lens (SCL) wearers with significant SCL-related dryness symptoms with and without significant ocular signs of dryness. METHODS: In a multicenter, prospective observational clinical trial, symptomatic SCL wearers reported significant SCL-related dryness via self-administered questionnaire of frequency and intensity of dryness after a dry eye (DE) examination. DE etiology was assigned post hoc by an expert panel, and those with and without significant DE-related signs were analyzed by univariate logistic regression. Possible DE etiologies were aqueous tear deficiency, SCL-induced tear instability, meibomian gland dysfunction, or "other." Wearers without signs that qualified for any DE etiology were designated as No DE Signs (NDES). RESULTS: Of the 226 SCL symptomatic wearers examined, 23% were without signs, 30% had aqueous tear deficiency, 25% had SCL-induced tear instability, 14% had meibomian gland dysfunction, and 8% had "other" diagnoses. The NDES wearers had significantly longer pre-lens break-up time (9.8 vs. 6.6 s, p < 0.0001), better lens wetting (3.4 vs. 2.4 0 to 4 scale, p < 0.0001), lower levels of film deposits on lenses (0.45 vs. 0.92, 0 to 4 scale, p < 0.0001), and of most slit lamp signs. The NDES wearers were significantly more likely to be male (36% vs.19%, p = 0.013), were less likely to have deteriorating comfort during the day (81% vs. 97%, p = 0.001), reported longer average hours of comfortable wear (11 ± 3 vs. 9 ± 4 h, p = 0.014), had older contact lenses (18 ± 14 vs. 13 ± 12 days, p = 0.029), and greater intensity of photophobia early and late in the day (p = 0.043 and 0.021). CONCLUSIONS: Symptoms of dryness in SCL wearers stem from a variety of underlying causes. However, nearly one-quarter of these symptomatic SCL wearers appear to be free of signs of dryness. The effective management of CL-related dryness requires a comprehensive range of clinical assessments and the use of a diverse range of management strategies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".