Identifying barriers to the use of ultrasound in the perioperative period: a survey of southwestern Ontario anesthesiologists
Bibliographic record
Abstract
BACKGROUND: Ultrasound (US) can be used for many perioperative procedures, but evidence is lacking as to its frequency of use and barrier of application. The objectives of this survey were to determine i) how often US guidance was used perioperatively for vascular access placement, nerve blocks, and heart and lung assessment, and ii) to identify the barriers and the limitations of using US amongst anesthesiologists in southwestern Ontario. METHODS: We conducted a web-based survey in over 40 academic or community hospitals at southwestern Ontario. RESULTS: Of 266 surveys sent, 66 complete surveys were obtained (response rate of 25%). Most respondents (> 80%) reported that US was commonly used for central venous catheter (CVC) insertion, followed by regional blocks; the uses were less frequent for neuraxial blockade and cardiopulmonary assessment. Most respondents wanted to use US more frequently as part of their practice and felt that they already had adequate US training. However, most respondents (59%) reported limited access to US machines in their working institutes as being the major barrier to incorporating US in their daily practice. CONCLUSION: The most common uses of US in anesthesia practice in southwestern Ontario were for CVC insertion and regional blocks. Most anesthesiologists in southwestern Ontario are interested to incorporate US in their daily practice but most were limited by the lack of US resources. Apparently, only providing knowledge and skills teaching may not be sufficient to further improve the US utilization in our region; a matched administrative effort appears to be the next challenge.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".