Development and validation of the Iris Glare, Appearance, and Photophobia questionnaire for patients with iris defects
Bibliographic record
Abstract
PURPOSE: To validate the Iris Glare, Appearance, and Photophobia (Iris GAP) questionnaire, a new symptom-based and appearance-based quality-of-life measure for patients with iris defects. SETTING: Single tertiary glaucoma clinic in Toronto, Ontario, Canada. DESIGN: Prospective cohort study. METHODS: Patients with varying degrees of iris defects were enrolled. Patients completed the Iris GAP questionnaire and the glare and driving subscales of the Refractive Status and Vision Profile (RSVP) questionnaire. Test-retest reliability, defined by Cronbach α and intraclass correlation coefficients (ICCs), was evaluated with repeat testing 2 weeks later. RESULTS: The study included 73 patients with iris defects, 68 controls with no iris defects, 77 patients with peripheral iridotomies (PIs) or transillumination defects (TIDs), and 22 patientswith surgically repaired irides (n = 22). Iris GAP scores ranged from 0 to 32 with a 97% completion rate. Iris GAP had high test-retest reliability (Cronbach α = 0.866, ICC = 0.953, P < .0005). Iris GAP scores were reliably distinguishable between patients with iris defects, repaired iris defects, and PIs and TIDs and controls (1-way analysis of variance, P < .0005). In pairwise comparisons, the major defect group had statistically significant higher scores than any of the other groups ( P < .005 for each). The control and repaired groups had the lowest scores, whereas the PI/TID group had intermediate scores. 9 patients underwent iris repair between tests and had a mean difference of 8.2 ± 6.2 points between their preoperative and postoperative scores ( P = .004). Iris GAP scores positively correlated with RSVP scores ( R2 = 0.73). CONCLUSIONS: Iris GAP can reliably evaluate symptomatology and patient-reported appearance in patients with iris defects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".