Development and Validation of a Visual Symptom–Specific Patient-Reported Outcomes Instrument for Adults With Cataract Intraocular Lens Implants
Bibliographic record
Abstract
PURPOSE: To develop a patient-reported outcome measure for capturing visual and ocular symptoms before and after implantation of intraocular lenses (IOLs) for treatment of cataracts. DESIGN: Questionnaire development and validation study. METHODS: The Questionnaire for Visual Disturbances (QUVID) was developed based on a literature and instrument review; 13 clinician interviews among ophthalmologists in the United States and Europe; and 67 hybrid qualitative patient interviews among adult patients in the United States and Australia before and/or after monofocal, traditional multifocal, or trifocal IOL implantation. Assessment of the QUVID's psychometric properties was conducted via a noninterventional cross-sectional study of previously treated cataract patients in the United States, Canada, and Australia (n = 150), and assessment of ability to detect meaningful change via 2 pivotal US clinical trials among patients with trifocal or extended vision IOL compared with monofocal IOL controls (n = 457). RESULTS: The QUVID includes subitems about the bothersomeness of 7 visual symptoms: starburst, halo, glare, hazy vision, blurred vision, double vision, and dark areas. The postoperative version contains 1 item asking the respondents whether their symptoms bothered them enough to want another surgery, if the IOL was the cause. CONCLUSIONS: The QUVID was reviewed by the US Food and Drug Administration and found appropriate as a fit-for-purpose measure, demonstrating requisite evidence for content validity, construct validity, reliability, and ability to detect change.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".