Precisión de la calculadora de riesgo quirúrgico ACS NSQIP para predecir morbilidad y mortalidad en pacientes mexicanos
Bibliographic record
Abstract
BACKGROUND: American College of Surgeons (ACS) developed the ACS NSQIP surgical risk calculator that predicts the results of elective and emergency surgical procedures. This tool has been useful improving the morbidity and mortality in hospitals in the United States and Canada. OBJECTIVE: To evaluate the usefulness of the ACS NSQIP risk calculator for predicting postoperative complications in Mexican population. METHOD: Prospective, observational, analytical study. Patients undergoing abdominal surgery were recorded, 21 preoperative variables were captured and entered into the calculator. They were followed up to 30 days postoperatively, identifying 14 types of postoperative complications. RESULTS: 109 patients were registered. A comparison was made between the calculated and observed complications, obtaining a good correlation in the complications of cardiac arrest, surgical site infection, reoperation, sepsis and mortality (p < 0.05). CONCLUSIONS: ACS NSQIP risk calculator is useful in the Mexican population, since the score obtained predicts most postoperative complications including mortality. The use of this tool offers an opportunity to improve decision-making in the care of the surgical patient.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".