Determinants of patient satisfaction with cataract surgery and length of time on the waiting list
Bibliographic record
Abstract
AIMS: To assess determinants of patient satisfaction with their waiting time (WT) and cataract surgery outcome. METHODS: A prospective cohort of consecutive patients waiting for cataract surgery were assessed by their ophthalmologist. Satisfaction, maximum acceptable waiting time (MAWT), urgency, visual function, visual acuity (VA), and health related quality of life (EQ-5D) were assessed using mailed questionnaires before surgery and 8-10 weeks after surgery. Ordinal logistic regression was used to build explanatory models. RESULTS: 166 patients (61.9% female, mean age 73.4 years) had a mean WT of 16 weeks. Patients whose actual WT was shorter than their MAWT had greater odds of being satisfied with their WT than those whose WT was longer (adjusted OR 3.86, 95% CI 1.38 to 10.74). Improvement in visual function (OR 3.19, 95% CI 1.78 to 5.73), and VA (OR 4.27, 95% CI 1.70 to 10.68) significantly predicted satisfaction with surgery. Models were adjusted for age and sex. CONCLUSION: Patient perspectives on MAWT and satisfaction with WT are important inputs to the process of determining WT standards for levels of patient priority. Patient expectation of WT may mediate satisfaction with actual WT.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".