Determination of the Minimal Clinically Important Difference (MCID) for Ocular Subjective Responses
Bibliographic record
Abstract
Purpose: To determine the minimal clinically important difference (MCID) for contact lens (CL)-related subjective responses and explore whether MCID values differ between subjective responses and study designs. Methods: This was a retrospective analysis of data from seven one-week bilateral crossover studies and 14 one-day contralateral CL studies. For comfort, dryness, vision, or ease of insertion, participants rated on a 0-100 visual analogue scale (VAS) and indicated lens preference on a five-point Likert scale featuring strong, slight, and no preferences. For each criterion, four MCID estimates were calculated and averaged: mean VAS score difference for "slight preference," lower limit of 95% confidence interval VAS score difference for "slight preference," difference in mean VAS score difference between "slight" and "no preference" and 0.5 standard deviation of VAS scores. Results: The four calculation methods generated a small range of MCID values. For bilateral studies, the averaged MCID was 7.2 (range 5.4-8.8) for comfort, 8.1 (5.2-10.6) for dryness, 7.1 (5.5-9.3) for vision and 7.6 (6.0-10.5) for ease of insertion. For contralateral studies, the averaged MCID was 6.9 (6.1-7.6) for comfort at insertion and 7.5 (6.8-8.2) for end-of-day comfort. Conclusions: This work demonstrated very similar MCID values across subjective responses and study designs, in a population of habitual soft CL wearers. In all cases, MCID values were on average seven units on a 0 to 100 VAS. Translational Relevance: This work provides MCID values which are important for interpreting ocular subjective responses and planning clinical studies.
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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 | 0.102 | 0.231 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".