FACTS Survey: Focused Assessment With Sonography in Trauma Use Among Canadian Residents Training in General Surgery
Bibliographic record
Abstract
BACKGROUND: A survey of all Canadian residents training in general surgery was conducted to determine the prevalence and nature of focused assessment with sonography in trauma (FAST) training. METHODS: A cross-sectional survey of all 549 residents in 16 Canadian general surgery programs was administered using the Tailored Design Method between December 2008 and February 2009. RESULTS: With a response rate of 58.5% (321 of 549), the prevalence of FAST training among Canadian residents was 21.2% (95% confidence interval: 17.2-25.2). The median number of practice and patient examinations completed was 5 (interquartile range [IQR]: 2-10.5) and 11.5 (IQR: 1.75-50), respectively. Only 38.8% of residents with training felt comfortable making treatment decisions based on their FAST examinations. Those residents who were comfortable had completed more practice and patient examinations (median, 12.5 vs. 4, p = 0.001 and 30 vs. 4.5, p ≤ 0.001, respectively) and were less likely to have didactic only training (7.7% vs. 19.5%, p = 0.002). Most residents (80%) indicated that they would need 20 practice examinations or more (median, 30 examinations; IQR, 20-40) before they would feel comfortable. Residents with FAST training were more likely to be from a program that offered FAST training (54.5% vs. 10%, p ≤ 0.001) and were less likely to perceive a turf war with other specialties over FAST use (29.9% vs. 48.2%, p = 0.007). CONCLUSIONS: The situation with FAST training in Canada seems inadequate with few general surgery residents being trained, and of those trained, only a few are comfortable with the technique. If FAST skills are to be expected of future surgeons, initiatives must be put in place to address barriers and improve training opportunities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".