Multicentre observational study of surgical system failures in aortic procedures and their effect on patient outcomes
Bibliographic record
Abstract
BACKGROUND: Vascular surgical care has changed dramatically in recent years with little knowledge of the impact of system failures on patient safety. The primary aim of this multicentre observational study was to define the landscape of surgical system failures, errors and inefficiency (collectively termed failures) in aortic surgery. Secondary aims were to investigate determinants of these failures and their relationship with patient outcomes. METHODS: Twenty vascular teams at ten English hospitals trained in structured self-reporting of intraoperative failures (phase I). Failures occurring in open and endovascular aortic procedures were reported in phase II. Failure details (category, delay, consequence), demographic information (patient, procedure, team experience) and outcomes were reported. RESULTS: There were strong correlations between the trainer and teams for the number and type of failures recorded during 88 procedures in phase I. In 185 aortic procedures, teams reported a median of 3 (i.q.r. 2-6) failures per procedure. Most frequent failures related to equipment (unavailability, failure, configuration, desterilization). Most major failures related to communication. Fourteen failures directly harmed 12 patients. Significant predictors of an increased failure rate were: endovascular compared with open repair (incidence rate ratio (IRR) for open repair 0·71, 95 per cent c.i. 0·57 to 0·88; P = 0·002), thoracic aneurysms compared with other aortic pathologies (IRR 2·07, 1·39 to 3·08; P < 0·001) and unfamiliarity with equipment (IRR 1·52, 1·20 to 1·91; P < 0·001). The major failure total was associated with reoperation (P = 0·011), major complications (P = 0·029) and death (P = 0·027). CONCLUSION: Failure in aortic procedures is frequently caused by issues with team-working and equipment, and is associated with patient harm. Multidisciplinary team training, effective use of technology and new-device accreditation may improve patient 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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".