Development of a novel formative assessment tool to assess intraoperative decision-making in colorectal surgery
Bibliographic record
Abstract
Intro: The objective of this study was to develop a clinically applicable nomogram to quantify the risk of 30-day mortality for patients who undergo surgery for fulminant C. difficile colitis (FCDC). Methods: After institutional board approval, the ACS-NSQIP database (2005-2015) was used to include adult patients who underwent emergency surgery (ASA u22653) for FCDC. CPT codes were limited to total abdominal colectomies (TAC). A priori preoperative predictors of mortality were selected from the literature: age, immunosuppression, sepsis, intubation, and laboratory values. Logistic regression models were fitted and the predictive accuracy of different models were measured by calculating the area under the receiver-operating characteristic (ROC) curve as well as the AIC and BIC criteria. A cohort of 124 patients from Quu00e9bec was used to validate the developed mortality calculator. Results: A total of 557 patients met our inclusion criteria and the overall mortality was 44%. The model with the best predictive accuracy included the following preoperative predictors (estimate [95%CI]): shock (0.66 [0.21;1.12), immunosuppression (1.84 [1.11;3.04]), creatinine (0.72 [0.26;1.17]), creatinine 2 (-0.11 [-0.18;0.04]), thrombocytopenia (-0.98 [-1.42;-0.54]), leukocyte count (between 4,000 and 20,000 cells/mm3 (0.44 [-0.76;1.63]), between 20,000 up to 50,000 cells/mm3 (0.34 [-0.85;-1.53]) and >50,000 cells/mm3 (3.41 [-0.22;7.04])) and age (-0.11 [-0.19;-0.02]). No statistically significant differences were found when comparing the predictive ability of the developed risk calculator with the validated ACS-NSQIP mortality risk calculator available in the database (AUC 75.61 vs. 75.14, p 0.79). External validation with the cohort of patients from Quebec showed an area under the ROC curve of 74.0% (95%CI 65.0-83.0)Conclusion:A clinically applicable calculator using preoperative variables to predict post-operative mortality for patients with FCDC was developed and externally validated. This calculator can help guide pre-operative decision-making.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".