A machine learning model to predict the need for conversion of operative approach in patients undergoing colectomy for neoplasm
Bibliographic record
Abstract
BACKGROUND: Studies comparing conversion from laparoscopic to open approaches to colectomy have found an association between conversion and morbidity, mortality, and length of stay, suggesting that certain patients may benefit from an open approach "up-front." AIM: The objective of this study was to use machine learning algorithms to develop a model enabling the prediction of which patients are likely to require conversion. METHODS AND RESULTS: We used ACS NSQIP data to identify patients undergoing colectomy (2014-2019). We included patients undergoing elective colectomy for colorectal neoplasm via a minimally invasive approach or a converted approach. The outcome of interest was conversion. Variables were included in the model based on their correlation with conversion by logistic regression (p < .05). Two models were used: weighted logistic regression with regularization, and Random Forest classifier. The data was randomly split into training (70%) and test (30%) cohorts, and prediction performance was calculated. 24 327 cases were included (17 028 training, 7299 test). When applied to the test cohort, the models had an accuracy of 0.675 (range 0.65-0.70) in predicting conversion; c-index ranged from 0.62-0.63. This machine learning model achieved a moderate area under the curve and a high negative predictive value, but a low positive predictive value; therefore, this model can predict (with 95% accuracy) whether a colectomy for neoplasm can be successfully completed using a minimally invasive approach. CONCLUSION: This model can be used to reassure surgeons of the appropriateness of a minimally invasive approach when planning for an elective colectomy.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".