© 2003 Canadian Medical Association or its licensors Letters
Bibliographic record
Abstract
Evaluating elective surgery Charles Wright and colleagues1 are tobe commended for the model of health outcome measures that they have developed for examining the appropriate-ness of elective surgical procedures. How-ever, we have some concerns about the tool used to assess cataract surgery. The VF-14 index is very sensitive when used appropriately, but it was not designed to determine who needs surgery. Previous studies of preoperative visual function have obtained findings similar to those of the Wright study, that is, that about 20% to 30 % of people have a high score on the visual function test.2 However, such results do not necessarily mean that these patients do not need surgery. The VF-14 index is a composite measure for reading, driving, playing sports, watching televi-sion and other activities, and as such it does not clearly identify people with a significant deficit in just one of these do-mains who would benefit from surgery. Ocular comorbidity, which was pre-sent in up to 50 % of all cataract patients in the cohort studied (Ken Bassett, As-sociate Professor, Department of Oph-thalmology, University of British Co-lumbia: personal communication, 2003), predicts poor outcomes. So does old age. Such comorbidity does not mean that surgery is inappropriate, but the VF-14 index does not capture patient satisfaction after surgery in such cases. Finally, the authors did not emphasize that ophthalmologists have reported vi-sual improvement in 92.4 % (786/851) of patients at the University of British Co-lumbia Eye Care Centre (essentially the same patients as were included in the Re-
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.561 | 0.370 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".