Predicting changes in cataract surgery health outcomes using a cataract surgery appropriateness and prioritization instrument
Bibliographic record
Abstract
OBJECTIVE: Determine whether items in a cataract surgery appropriateness and prioritization questionnaire can predict change in best corrected visual acuity (BCVA) and health related quality of life (HRQOL) following cataract surgery. METHODS: 313 patients with a cataract in Ontario, Canada were recruited to participate. BCVA was measured using the Snellen chart. HRQOL was measured using a generic instrument (EQ5D), a visual functioning instrument (Catquest-9SF), and an appropriateness and prioritization instrument (17 items). Outcomes were measured preoperatively and 3-6 months postoperatively. Descriptive statistics were used to describe demographics and outcomes. For each appropriateness and prioritization questionnaire item, a one-way ANOVA was used to compare group means of the change in BCVA, EQ5D, and Catquest-9SF. RESULTS: Participants had a mean age of 69 years and were 56% female. BCVA improved in 81%, EQ5D in 49.6%, and Catquest-9SF score in 84% of patients. Improvement in both BCVA and Catquest-9SF scores were found in 68.5% of patients. The ANOVA showed a statistically significant association between a change in BCVA and the ability to participate in social life, and a statistically significant association between a change in Catquest-9SF and glare, extent of impairment in visual function, safety and injury concerns, ability to work and care for dependents, ability to take care of local errands, ability to assist others and ability to participate in social life. CONCLUSIONS: Almost all patients had improved BCVA and/or visual functioning after surgery. Seven variables from the cataract appropriateness and prioritization instrument were found to be predictors of improvement in Catquest-9SF measuring visual functioning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".