Attitudes of Canadian general surgery staff and residents towards point-of-care ultrasound
Bibliographic record
Abstract
Background: Point-of-care ultrasound (POCUS) has been introduced in the training curricula of several residency programs. However, it is yet to be routinely integrated into general surgery (GS). This study aims to identify the attitudes of the GS Canadian academic community towards POCUS. Methods: A multiple-choice survey was sent to all Canadian GS programs. The survey was comprised of three sections: baseline characteristics, current perceived knowledge of POCUS, and barriers to POCUS implementation. Results: The targeted sample included 609 surgeons and 593 residents (a total of 1202). Of these, 58 surgeons and 79 residents responded (11.3% response rate). Overall, only 5.2% reported using POCUS daily, and 44.8% of the staff surgeons reported never using POCUS. The most reported indications included extended focused assessment with sonography in trauma (eFAST) and the insertion of central lines. Staff surgeons were reluctant to operate solely on the findings of POCUS. The perceived sensitivity of POCUS for various surgical indications was significantly lower than that reported in the literature. Examples include diagnosing the etiology of shock where only 58.6% of staff and 50.6% of residents chose the correct answer reported in the literature for that indication. However, for diagnosing cholecystitis, only 46.6% of staff and 34% of residents responded correctly as per the literature. The majority of the residents (69.5%) believed that POCUS should be implemented in training programs. Perceived barriers to POCUS implementation included lack of time for training, lack of confidence in POCUS, and concerns about medicolegal consequences. Conclusion: This study reveals the need to support POCUS training by GS residents despite their low current usage. Addressing barriers to its implementation and knowledge gaps regarding POCUS could lead to its wider adoption.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".