The Effect of Technical Performance on Patient Outcomes in Surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it