A survey of ultrasound use by academic and community anesthesiologists in Ontario
Bibliographic record
Abstract
PURPOSE: The use of ultrasound for central venous catheter (CVC) insertion and regional anesthesia (RA) has been recommended to improve efficacy and patient safety. We conducted a survey to evaluate the degree to which ultrasound use has been adopted into routine practice by anesthesiologists in the province of Ontario, Canada. METHODS: Following approval by the Research Ethics Board at The Hospital for Sick Children, we conducted a web-based survey of anesthesiologists registered with the College of Physicians and Surgeons of Ontario. The anesthesiologists surveyed were working in academic or community hospitals. The survey elicited information on the degree of routine use of ultrasound for CVC or RA blocks, reasons for non-use of ultrasound, and methods of ultrasound training. RESULTS: A questionnaire was sent to 450 anesthesiologists via e-mail. There were 209 (46%) respondents, six of whom were excluded as the anesthesiologists were no longer in practice, resulting in 203 responses for analysis. Of these, 163 anesthesiologists practiced in academic hospitals, and 40 practiced in community hospitals. A larger proportion of academic (60%) vs community (33%) anesthesiologists use ultrasound routinely for CVC insertion (P = 0.004). Routine use for RA blocks was comparable in both groups. The most common reason given for non-use of ultrasound for CVC insertion was "ultrasound is unnecessary for safe/effective insertion of CVCs". Peer-to-peer training was the most preferred method for improving ultrasound skills. CONCLUSIONS: The use of ultrasound is better established in academic than in community anesthesia practice. Anesthesiologists in community practice appear to be adopting ultrasound at a slower pace, which may be explained by lack of equipment and lack of training.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".