Plastic surgery wait times in Ontario: A potential surrogate for workforce demand
Bibliographic record
Abstract
A ccurately projecting physician workforce requirements is an important component of ensuring a high standard of medical care and judicious use of scarce health care resources.It is essential to have an adequate supply of well-educated providers to meet current and future health care needs.Accurate prediction of physician workforce requirements involves considering the factors that affect both physician supply and demand (Table 1).These factors include projecting future population characteristics, the need for future services, and estimating the future practice patterns of physicians and availability of health care resources.This is not a straightforward task.Longstanding concerns of impending physician surpluses have been replaced with growing awareness of physician shortages across all fields of medicine (1-3).In plastic surgery, studies of surgeon workforce requirements have been limited.In 1993, a study commissioned by the American Society of Plastic Surgeons predicted a 35% increase in the number of plastic surgeons by 2020, even with significant reductions (40%) in training and education (4).On the other hand, in 2007, Macadam et al (5) surveyed Canadian plastic surgeons to determine perceived surgeon supply.Limited by a 42% response rate and self-report, 78% of respondents believed that there were not enough plastic surgeons.Wait times for elective, noninsured and urgent consultation were 32 weeks, 11 weeks and 11 days, respectively.The authors hypothesized that to maintain current ratios of plastic surgeons in Canada, surgeon training would need to increase by 10 graduates per year.Rohrich et al (6) performed OriginAl Article
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".