Bibliographic record
Abstract
AIM: To determine whether there has been a consistent change across countries and healthcare systems in the frequency of strabismus surgery in children over the past decade. METHODS: Retrospective analysis of data on all strabismus surgery performed in NHS hospitals in England and Wales, on children aged 0-16 years between 1989 and 2000, and between 1994 and 2000 in Ontario (Canada) hospitals. These were compared with published data for Scotland, 1989-2000. RESULTS: Between 1989 and 1999-2000 the number of strabismus procedures performed on children, 0-16 years, in England decreased by 41.2% from 15 083 to 8869. Combined medial rectus recession with lateral rectus resection decreased from 5538 to 3013 (45.6%) in the same period. Bimedial recessions increased from 489 to 762, oblique tenotomies from 43 to 121, and the use of adjustable sutures from 29 to 44, in 2000. In Ontario, operations for squint decreased from 2280 to 1685 (26.1%) among 0-16 year olds between 1994 and 2000. CONCLUSION: The clinical impression of decrease in the frequency of paediatric strabismus surgery is confirmed. In the authors' opinion this cannot be fully explained by a decrease in births or by the method of healthcare funding. Two factors that might have contributed are better conservative strabismus management and increased subspecialisation that has improved the quality of surgery and the need for re-operation. This finding has a significant impact upon surgical services and also on the training of ophthalmologists.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".