A Multidimensional Approach of Surgical Mortality Assessment and Stratification (Smatt Score)
Bibliographic record
Abstract
Surgical mortality is the most significant measure of outcome in surgical healthcare. The objective was to assess surgical 30 days mortality and improve the identification of predictors for personalized risk stratification of patients undergoing elective and emergency surgery. The study was conducted as a single-center cohort retrospective observational study, based on the analysis of data collected from patients surgically treated from 2002 to 2014 in a multi-disciplinary research and care referral hospital with global case mix of 1.27. The overall in-hospital mortality rate was 1.89% (95% CI 1.82-1.95). In the univariable analysis, numerous predictors were significantly associated with in-hospital death following surgery. In the multivariable model, age, BMI (Body Mass Index), ASA score, department, planned surgical complexity, surgical priority, previous surgeries in the same hospitalization, cardiovascular, pulmonary, hepato-renal comorbidities, drug intolerance, cancer and AIDS were independently associated with mortality after surgery. At logistic regression, the computed SMATT score (graded 0-100), generated on the basis of multivariate analysis, demonstrated a good discrimination (10-fold cross-validated AUC-ROC 0.945, 95%CI 0.941-0.948) and correctly classified 98.5% of those admissions with a probability of death >50%. The novel SMATT score, based on individual preoperative and surgical factors, accurately predicts mortality and provides dynamic information of the risk in redo/reoperative surgery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".