Impact of orthopedic trauma consolidation on resident education.
Bibliographic record
Abstract
BACKGROUND: In July 2005, the Saskatoon Health Region, in conjunction with its orthopedic surgeons, consolidated its management of orthopedic emergencies from 3 sites to 1, resulting in trauma patients being directed to the university hospital site (Royal University Hospital; RUH) where orthopedic residents were the first responders. We sought to measure resident workload in the emergency department, operating room and ward before and after consolidation and to measure the perceptions of residents and faculty on the newly established orthopedic trauma service. METHODS: We compared orthopedic volumes at RUH in the emergency department (ED) and trauma-related operating rooms over 2 periods of 3 months' duration before and after trauma consolidation. We developed and disseminated questionnaires evaluating issues relating to patient care; resident education, including all CanMEDS domains; resident well-being; and the orthopedic trauma service to all orthopedic residents and faculty members. RESULTS: The number of patients seen by orthopedic residents in the ED increased by 67%, the number admitted through the ED to the ward increased by 66%, and the total number of inpatients and consultations increased by 43% after the consolidation. The number of patients processed through the orthopedic emergency operating room increased by 90%. In general, response to the change was positive and appreciated by residents and faculty members. CONCLUSION: Sudden substantial increases in the volume of patients seen by orthopedic residents may not prompt negative resident responses when the overall gains offset, if not exceed, the perceived losses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".