Abstract 14811: A Clinical Risk Scoring Tool to Predict Readmission After Discharge From Cardiac Surgery: An Administrative and Clinical Database Study
Bibliographic record
Abstract
Introduction: Reducing readmission after cardiac surgery remains a quality improvement priority. Most readmission risk models examine only coronary artery bypass grafting (CABG). We elucidate the pre- and peri-operative risk factors for readmission after CABG and/or valve surgery using administrative databases in Ontario, Canada. Methods: Adults >18 years undergoing isolated CABG, isolated/multiple valve (aortic, mitral or tricuspid) or combined CABG/valve surgery from 2008-2016 in Ontario were eligible. Risk factors for 30-day readmission after discharge were obtained through linkages of Ontario administrative databases. Hazard ratios for risk factors were calculated using Cox proportional hazards regression. We developed a clinical risk scoring tool weighted by beta-coefficients from the final model. Discrimination and calibration was performed using c-statistics and comparing the predicted to observed probabilities across deciles of predicted risk. Results: 63,336 patients underwent CABG and/or valve s...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".