Multi-center validation of Catquest-9SF visual function questionnaire in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: To investigate the psychometric performance and responsiveness of Catquest-9SF, a patient-reported questionnaire developed to evaluate visual function as related to daily tasks, in patients referred for cataract surgery in Ontario, Canada. METHODS: This is a pooled analysis on prospective data collected for previous projects. Subjects were recruited from three tertiary care centers in Peel region, Hamilton, and Toronto, Ontario, Canada. Catquest-9SF was administered pre-operative and post-operatively to patients with cataract. Psychometric properties, including category threshold order, infit/outfit, precision, unidimensionality, targeting, and differential item functioning were tested using Rasch analysis with Winsteps software (v.4.4.4) for Catquest-9SF. Responsiveness of questionnaire scores to cataract surgery was assessed. RESULTS: 934 patients (mean age = 71.6, 492[52.7%] female) completed the pre- and post-operative Catquest-9SF questionnaire. Catquest-9SF had ordered response thresholds, adequate precision (person separation index = 2.01, person reliability = 0.80), and confirmed unidimensionality. The infit range was 0.75-1.29 and the outfit range was 0.74-1.51, with one item ('satisfaction with vision') misfitting (outfit value = 1.51). There was mistargeting of -1.07 in pre-operative scores and mistargeting of -2.43 in both pre- and post-operative scores, meaning that tasks were relatively easy for respondent ability. There was no adverse differential item functioning. There was a mean 1.47 logit improvement in Catquest-9SF scores after cataract surgery (p<0.001). CONCLUSION: Catquest-9SF is a psychometrically robust questionnaire for assessment of visual function in patients with cataract in Ontario, Canada. It is also responsive to clinical improvement after cataract surgery.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".