Technical Performance as a Predictor of Clinical Outcomes in Laparoscopic Gastric Cancer Surgery
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the relationship between technical performance and patient outcomes in laparoscopic gastric cancer surgery. BACKGROUND: Laparoscopic gastrectomy for cancer is an advanced procedure with high rate of postoperative morbidity and mortality. Many variables including patient, disease, and perioperative management factors have been shown to impact postoperative outcomes; however, the role of surgical performance is insufficiently investigated. METHODS: A retrospective review was performed for all patients who had undergone laparoscopic gastrectomy for cancer at 3 teaching institutions between 2009 and 2015. Patients with available, unedited video-recording of their procedure were included in the study. Video files were rated for technical performance, using Objective Structured Assessments of Technical Skills (OSATS) and Generic Error Rating Tool instruments. The main outcome variable was major short-term complications. The effect of technical performance on patient outcomes was assessed using logistic regression analysis with backward selection strategy. RESULTS: Sixty-one patients with available video recordings were included in the study. The overall complication rate was 29.5%. The mean Charlson comorbidity index, type of procedure, and the global OSATS score were included in the final predictive model. Lower performance score (OSATS ≤29) remained an independent predictor for major short-term outcomes (odds ratio 6.49), while adjusting for comorbidities and type of procedure. CONCLUSIONS: Intraoperative technical performance predicts major short-term outcomes in laparoscopic gastrectomy for cancer. Ongoing assessment and enhancement of surgical skills using modern, evidence-based strategies might improve short-term patient outcomes. Future work should focus on developing and studying the effectiveness of such interventions in laparoscopic gastric cancer surgery.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".