Development and validation of a clinical risk calculator for mortality after colectomy for fulminant Clostridium difficile colitis
Bibliographic record
Abstract
BACKGROUND: Clostridium difficile colitis is an increasingly important cause of morbidity and mortality. Fulminant C. difficile colitis (FCDC) is a severe form of the colitis driven by a significant systemic inflammatory response, and managed with a total abdominal colectomy. Despite surgery, postoperative mortality rates remain high. The aim of this study was to develop a bedside calculator to predict the risk of 30-day postoperative mortality for patients with FCDC. METHODS: After institutional review board approval, the American College of Surgeons National Surgical Quality Improvement Program database (2005-2015) was used to include adult patients who underwent emergency surgery for FCDC. A priori preoperative predictors of mortality were selected from the literature: age, immunosuppression, preoperative shock, intubation, and laboratory values. The predictive accuracy of different logistic regression models was measured by calculating the area under the receiver-operating characteristic curve. A cohort of 124 patients from Québec was used to validate the developed mortality calculator. RESULTS: A total of 557 patients met the inclusion criteria, and the overall mortality was 44%. After developing the calculator, no statistically significant differences were found in comparison with the American College of Surgeons National Surgical Quality Improvement Program probability of mortality available in the database (area under the receiver operating curve, 75.61 vs. 75.14; p = 0.79). External validation with the cohort of patients from Quebec showed an area under the curve of 74.0% (95% confidence interval, 65.0-82.9). CONCLUSION: A clinically applicable calculator using preoperative variables to predict postoperative mortality for patients with FCDC was developed and externally validated. This calculator may help guide preoperative decision making. LEVEL OF EVIDENCE: Prognostic and epidemiological study, level III.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".