The experts debate: Perioperative beta-blockade for noncardiac surgery—proven safe or not?
Bibliographic record
Abstract
Guidelines on perioperative management of patients undergoing noncardiac surgery recommend the use of prophylactic perioperative beta-blockers in high-risk patients who are not already taking them, and their continuance in patients on chronic beta-blockade prior to surgery. These recommendations were challenged recently by results of the Perioperative Ischemic Evaluation (POISE), a large randomized trial of extended-release metoprolol succinate started immediately before noncardiac surgery in patients at high risk for atherosclerotic disease. While metoprolol significantly reduced myocardial infarctions relative to placebo in POISE, it also was associated with significant excesses of both stroke and mortality. The merits and limitations of POISE and its applicability in light of other trials of perioperative beta-blockade are debated here by two experts in the field-Dr. Don Poldermans and Dr. P. J. Devereaux (co-principal investigator of POISE).
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 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 | 0.089 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.049 | 0.055 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".