The Effect of Technical Performance on Patient Outcomes in Surgery
Bibliographic record
Abstract
OBJECTIVE: Systematic review of the effect of intraoperative technical performance on patient outcomes. BACKGROUND: The operating room is a high-stakes, high-risk environment. As a result, the quality of surgical interventions affecting patient outcomes has been the subject of discussion and research for years. METHODS: MEDLINE, EMBASE, PsycINFO, and Cochrane databases were searched. All surgical specialties were eligible for inclusion. Data were reviewed in regards to the methods by which technical performance was measured, what patient outcomes were assessed, and how intraoperative technical performance affected patient outcomes. Quality of evidence was assessed using the Medical Education Research Study Quality Instrument (MERSQI). RESULTS: Of the 12,758 studies initially identified, 24 articles (7775 total participants) were ultimately included in this review. Seventeen studies assessed the performance of the faculty alone, 2 assessed both the faculty and trainees, 1 assessed trainees alone, and in 4 studies, the level of the operating surgeon was not specified. In 18 studies, a performance assessment tool was used. Patient outcomes were evaluated using intraoperative complications, short-term morbidity, long-term morbidity, short-term mortality, and long-term mortality. The average MERSQI score was 11.67 (range 9.5-14.5). Twenty-one studies demonstrated that superior technical performance was related to improved patient outcomes. CONCLUSIONS: The results of this systematic review demonstrated that superior technical performance positively affects patient outcomes. Despite this initial evidence, more robust research is needed to directly assess intraoperative technical performance and its effect on postoperative patient outcomes using meaningful assessment instruments and reliable processes.
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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 | 0.020 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".