Maximizing Surgical Success by Aligning Interventions to Outcomes: A Systematic Review
Bibliographic record
Abstract
Objective: This study aimed to identify common intraoperative interventions in surgery and evaluate their effectiveness in improving surgical outcomes. Background: Despite decades of efforts, surgical adverse events remain stubbornly high. There are concerns that too much responsibility is placed on individuals to create change (ie, person-based interventions) rather than adapting systems to support human performance (ie, system-based interventions). This focus may be due to our limited understanding of which interventions most effectively improve outcomes. Methods: A 2-step search was conducted. Systematic and meta-analytic reviews of Medline, CINAHL, Embase, PsycINFO, Scopus, Cochrane Reviews, Cochrane Protocols and Cochrane Trials were identified, and individual studies within these reviews were selected. Qualitative content analysis categorized intervention and outcome types using inductive and deductive methods. Intervention details and directional findings for all outcomes were extracted. Results: A total of 575 studies were included in the final analysis comprising 5,288,513 cases, 25,435 providers and patients, 2608 hospitals, across 50 countries, with 1221 outcomes extracted. Overall, the most common interventions were person-based, including education (38%) and policy (19%). Person-based interventions were more likely to improve interpersonal outcomes such as culture, professional development, and resilience. In contrast, system-based interventions, such as technology (15%), cognitive aids (11%), equipment (11%), standardization (4%), and environment redesign (2%), though less frequently implemented, were effective across all outcome types. Conclusions: Although person-based interventions are widely implemented, system-based interventions generally have a greater impact on surgical outcomes. These results offer valuable insights for optimizing the alignment of interventions to outcomes.
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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 | 0.025 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".