Development of a Prediction Model for Days at Home after Surgery in Patients Undergoing Elective Gastrointestinal Cancer Surgery
Bibliographic record
Abstract
Days at home (DAH) after surgery is a novel outcome measure that captures time spent in multiple health care institutions after surgery. As a patient-centred measure it has potential as a tool for both research and quality initiatives, while also having the potential to improve patient readiness for surgery. We performed a population-based study of adults who underwent elective gastrointestinal (GI) cancer surgery between 2003 to 2021 in Ontario to (1) evaluate DAH as a quality and research measure, and (2) develop and internally validate a prediction model for DAH 90 days after GI cancer surgery. A total of 89,378 patients were included in the study cohort, with an overall median DAH-90 of 82 (IQR 77 - 85). Criterion validity of DAH-90 was demonstrated through both concurrent and predictive validity. After comparing four regression strategies, the prediction model was developed using quantile regression at the median. Internal validation was performed using bootstrap methods with 500 repetitions. The final optimism correct prediction metrics are as follows: calibration slope of 1.0, mean absolute error of 8.68, and g-index of 3.26. This model was developed using best practice guidelines and a pragmatic lens with plans for external validation before translation into a user-friendly online tool.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".