Analysis of vitreoretinal surgery activity in metropolitan France in 2016: impact on training capacities
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to describe vitreoretinal surgery activity and vitreoretinal surgeons in private or public practice in metropolitan France over the year 2016 to anticipate surgical training needs. METHODS: Patients aged ≥20 years who had undergone vitreoretinal surgery, alone or combined with cataract surgery were included using the French National Healthcare system database. For surgery performed by ophthalmologists carrying out ≥50 procedures during the year, the incidence per 100 000 of population ≥ 20 years of age, the number and mean age of surgeons and the number of surgeons aged >55 years were calculated. RESULTS: Overall, 57 947 posterior segment surgical procedures were included, 40% in the public sector and 49% in the private sector for private surgeons and/or public centres performing ≥50 procedures/year. The remaining 11% of procedures were from private surgeons and/or public centres performing <50 procedures/year. The analysis included 356 surgeons with a mean age of 41 ± 10 years (39% female) in the public sector and 47 ± 10 years (14% female) in the private sector. The majority of urgent surgery was for retinal detachment (n = 30 290 [52% of total surgical procedures]). Scheduled surgery involved surgery for macular holes and epiretinal membranes (n = 16 454 [28% of total surgical procedures]). Combined vitrectomy-phacoemulsification surgery (n = 10 120) represented 17% of all vitreoretinal surgery. University regions with the fewest surgeons and regions with surgeons >55 years of age were identified, to anticipate the training need for new surgeons. CONCLUSION: This study demonstrated disparities in the geographic distribution of vitreoretinal surgery in France and identified regions that need increased training capacities to ensure a sufficient number of surgeons.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".