Clinical Evidence, Practice Guidelines, and β-Blocker Utilization Before Major Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Largely on the basis of 2 randomized trials published in the 1990s, β-blockers were initially promoted as an evidence-based intervention for preventing cardiac complications of noncardiac surgery. However, subsequent studies raised concerns about a widespread use of perioperative β-blockade. Little is known regarding how this changing evidence influenced the use of perioperative β-blockers in clinical practice. METHODS AND RESULTS: We conducted a population-based, time-series analysis (April 1999 to March 2010) among residents of Ontario, Canada (age 66 years and older), to evaluate the influence of research publications and practice guidelines on rates of new β-blocker prescriptions before major elective noncardiac surgery. In an analysis of 249 828 procedures, the rate of new β-blocker prescriptions increased from 26.3 per 1000 procedures in April 1999 to 62.7 per 1000 procedures in the first quarter of 2005, after which it decreased to 19.7 per 1000 procedures by March 2010. We observed a marked decrease in prescriptions (P=0.004) during early 2005, without any preceding publications that raised concerns about perioperative β-blockade. There was no change (P=0.98) in prescription rates after the May 2008 publication of a multicenter, randomized trial that showed increased mortality from perioperative β-blockade. Prescribing trends remain unchanged after revisions of related practice guidelines in 2002 (P=0.28) and 2006 (P=0.53). CONCLUSIONS: After a period characterized by increasing adoption of preoperative β-blockade between 1999 and 2005, prescriptions rates subsequently fell from 2005 to 2010. Further research is needed to understand the basis for these changes, which are only partially explained by evidence of potential harm.
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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 | 0.028 | 0.253 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".