Pediatric fractures – an educational needs assessment of Canadian pediatric emergency medicine residents
Bibliographic record
Abstract
OBJECTIVES: To determine the gaps in knowledge of Canadian pediatric emergency medicine residents with regards to acute fracture identification and management. Due to their predominantly medical prior training, fractures may be an area of weakness requiring a specific curriculum to meet their needs. METHODS: A questionnaire was developed examining comfort level and performance on knowledge based questions of trainees in the following areas: interpreting musculoskeletal X-rays; independently managing pediatric fractures, physical examination techniques, applied knowledge of fracture management, and normal development of the bony anatomy. Using modified Dillman technique the instrument was distributed to pediatric emergency medicine residents at seven Canadian sites. RESULTS: Out of 43 potential respondents, 22 (51%) responded. Of respondents, mean comfort with X-ray interpretation was 69 (62-76 95% confidence interval [CI]) while mean comfort with fracture management was only 53 (45-63 95% CI); mean comfort with physical exam of shoulder 60 (53-68 95% CI) and knee 69 (62-76 95% CI) was low. Less than half of respondents (47%; 95% CI 26%-69%) could accurately identify normal wrist development, correctly manage a supracondylar fracture (39%; 95% CI 20%-61%), or identify a medial epicondyle fracture (44%; 95% CI 24%-66%). Comfort with neurovascular status of the upper (mean 82; 95% CI 75-89) and lower limb (mean 81; 95% CI 74-87) was high. INTERPRETATION: There are significant gaps in knowledge of physical exam techniques, fracture identification and management among pediatric emergency medicine trainees. A change in our current teaching methods is required to meet this need.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".