Canadian radiology residents' knowledge of sedation and analgesia: a web-based survey.
Bibliographic record
Abstract
OBJECTIVE: An increasing number of procedures in the radiology department require the use of conscious sedation, with the agents often administered by the radiologist. We sought to determine the level of understanding of the nature and use of such agents in Canadian radiology residents. METHODS: A Web-based questionnaire was distributed to residents from 8 Canadian radiology residency programs. The questions concerned the pharmacology of common medications for conscious sedation, their indications and appropriateness for use, and the experience and attitudes of residents toward formal training in conscious sedation. RESULTS: A total of 178 surveys were dispersed and yielded an adjusted response rate of 51%. Most residents stated that they had not received any formal training in conscious sedation (65%) and were in favour (68%) of having such training. Although the residents typically correctly prescribed appropriate dosages of lorazepam (54%), midazolam (51%), and fentanyl (58.7%), excessively high dosages of midazolam were ordered by 15.9% of the residents. Knowledge regarding the onset of action and duration of commonly used medications was poor. Residents gave the correct response with regard to duration of action for lorazepam (23.8%), midazolam (31.9%), diazepam (15.9%), and fentanyl (28.6%). The correct responses to onset of action were as follows: for fentanyl, 22.2%; for midazolam, 19.1%; for lorazepam, 6.35%; and for diazepam, 11.1%. Residents were uncertain regarding the maximum dosage of local anesthetics that a patient could receive, with 1.5% and 20.6% correct responses regarding bupivacaine and lidocaine, respectively. CONCLUSION: Despite the recent publication of conscious sedation guidelines for nonanesthesiologists, this survey suggests that Canadian radiology residents are not receiving adequate training in the use of medications required for conscious sedation.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".