Physician and patient concordance in reporting of appropriateness and prioritization for cataract surgery
Bibliographic record
Abstract
BACKGROUND/AIMS: Determine the association between physician-deemed and patient-reported appropriateness and prioritization for cataract surgery. METHODS: Prospective cohort study of 471 patients of 7 ophthalmologists referred for cataract surgery. Ophthalmologists rated patients for cataract surgery appropriateness and prioritization using a visual analogue scale of 0-10 preoperatively. All patients completed the eCAPS Quality of Life (QoL), while 313 completed the Catquest-9SF and EQ-5D questionnaires. Regression analyses were applied to determine demographic, clinical and patient-reported outcome measures (PROMs) associated with appropriateness and prioritization. RESULTS: Two clinical factors (study eye and fellow eye best-corrected visual acuity (BCVA)), 2 eCAPS (night driving difficulty, ability to take care of local errands), and 2 Catquest-9SF PROMs (recognizing faces, seeing to walk on uneven ground) were associated with appropriateness. In multivariable regression, the rating physician, 2 clinical criteria (study eye BCVA, anticipated postoperative BCVA) and 1 eCAPS QoL (night driving difficulty) were associated with appropriateness. Prioritization was associated with low income, 8 clinical criteria, 9 eCAPS, 5 Catquest-9SF, and 1 EQ-5D PROMs. In multivariable regression, 1 clinical criterion (study eye BCVA), 2 eCAPS QoL (night driving difficulty, ability to take care of local errands), and 2 Catquest-9SF PROMs (seeing prices, seeing to walk on uneven ground) were significantly associated. CONCLUSIONS: The eCAPS and Catquest-9SF questionnaires show some concordance with physician-deemed appropriateness, and more with prioritization. Binary conversions of PROM scales provide similar modelling, with minimal loss of explanatory power. As physician-deemed appropriateness and prioritization do not completely capture the patient perspective, PROMs may have a role in cataract surgery decision-making frameworks.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".