Knowledge and Practice Regarding Prophylactic Perioperative Beta Blockade in Patients Undergoing Noncardiac Surgery: A Survey of Canadian Anesthesiologists
Bibliographic record
Abstract
UNLABELLED: A lack of awareness of the "best" current practice is frequently cited as a major barrier to the practice of evidence-based medicine. The purpose of this study was to survey Canadian anesthesiologists to determine their knowledge and practices associated with prophylactic perioperative beta blockade, a therapy that has been widely discussed in the literature and has the potential for a significant positive impact on patient outcomes. We sent questionnaires to 1234 members of the Canadian Anesthesiologists' Society. The overall response rate was 54%. Ninety-five percent of respondents were aware of the perioperative beta blocker literature, and of these, 93% agreed that beta blockers were beneficial in patients with known coronary artery disease (CAD). Fifty-seven percent reported always or usually administering prophylactic beta blockers in patients with known CAD, and 34% of these regular users continued therapy beyond the early postoperative period. Only 9% of respondents reported that a formal protocol existed at their facility. This study suggests that barriers to the translation of research to practice were not related to a lack of awareness of the current best evidence. With respect to perioperative beta blockers, controversies within the literature as well as practical considerations may be greater barriers to implementation of best evidence. IMPLICATIONS: This survey found that anesthesiologists were aware of and supported the use of prophylactic perioperative beta blockers in patients with risk factors or known coronary artery disease; however, only 57% frequently prescribed perioperative beta blockers. A lack of awareness of the current "best" evidence was not a barrier to use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".