Plastic surgery wait times in Ontario: A potential surrogate for workforce demand
Bibliographic record
Abstract
BACKGROUND: Accurate projections of plastic surgeon workforce requirements are essential to ensure a high standard of care and to properly allocate health care resources. Wait-time data were used to identify geographical areas that may benefit from additional plastic surgeons. METHODS: Plastic surgery wait times were analyzed using data from Ontario's Wait Time Information System for 2009 to 2010. Data were compared with benchmarks published by the Canadian Society of Plastic Surgeons, and plastic surgeon density was captured by the Ontario Physician Human Resources Data Centre. RESULTS: Aggregate plastic surgery wait times at the 90th percentile failed to meet targets based on priority. For priority 2 (target = 28 days) and priority 3 cases (target = 84 days), wait times were 35 and 101 days, respectively (P<0.05). Wait times also consistently exceeded provincial standards in the southwestern (Local Health Integration Network [LHIN] 2), eastern (LHINs 10 and 11) and northeastern (LHIN 13) regions of Ontario. A negative correlation (r=-0.37; P<0.05) between wait times and surgeon density for priority 4 cases was observed, suggesting that more surgeons per capita is associated with shorter wait times for these lower-priority cases. In contrast, a positive correlation was observed for priority 2 (r=0.50; P<0.05) and priority 3 cases (r=0.35; P<0.05). CONCLUSION: Plastic surgery wait times in Ontario exceeded benchmarks in several geographical regions. Paradoxically, for high-priority cases, wait times were longer in LHINs with a higher density of plastic surgeons. Further investigation into patient mobility, physician practice patterns and the availability of hospital resources, such as hospital beds or operating room time, is required.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".