Correction to: Anesthesia interventions that alter perioperative mortality: a scoping review
Bibliographic record
Abstract
With over 230 million surgical procedures performed annually worldwide, better application of evidence in anesthesia and perioperative medicine may reduce widespread variation in clinical practice and improve patient care. However, a comprehensive summary of the complete available evidence has yet to be conducted. This scoping review aims to map the existing literature investigating perioperative anesthesia interventions and their potential impact on patient mortality, to inform future knowledge translation and ultimately improve perioperative clinical practice. Searches were conducted in MEDLINE, EMBASE, CINAHL, and the Cochrane Library databases from inception to March 2015. Study inclusion criteria were adult patients, surgical procedures requiring anesthesia, perioperative intervention conducted/organized by a professional with training in anesthesia, randomized controlled trials (RCTs), and patient mortality as an outcome. Studies were screened for inclusion, and data was extracted in duplicate by pairs of independent reviewers. Data were extracted, tabulated, and reported thematically. Among the 10,505 publications identified, 369 RCTs ( n = 147,326 patients) met the eligibility criteria. While 15 intervention themes were identified, only 7 themes (39 studies) had a significant impact on mortality: pharmacotherapy ( n = 23), nutritional ( n = 3), transfusion ( n = 4), ventilation ( n = 5), glucose control ( n = 1), medical device ( n = 2), and dialysis ( n = 1). By mapping intervention themes, this scoping review has identified areas requiring further systematic investigation given their potential value for reducing patient mortality as well as areas where continued investment may not be cost-effective given limited evidence for improving survival. This is a key starting point for future knowledge translation to optimize anesthesia practice.
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 | 0.026 | 0.278 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.071 | 0.025 |
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".