Prediction of Complications in Radical Prostatectomy Prostate Cancer Patients: Simulated Annealing versus Co-Morbidity Indexes
Bibliographic record
Abstract
BACKGROUND: The Deyo/Charlson co-morbidity index (CCI) and Klabunde co-morbidity index (KCI) co-morbidity indexes represent outdated indexes when the endpoint of complications after radical prostatectomy (RP) is considered. A novel group of co-morbidities derived from International Classification of Diseases-9 diagnostic codes in a contemporary RP database could provide better accuracy. Research Design, Subjects and Measures: We relied on 20,484 patients with clinically localized non-metastatic prostate cancer treated with RP between 2000 and 2009 in the Surveillance, Epidemiology, and End Results-Medicare linked database. We examined 2 endpoints, namely, 90-day medical complication rate and 90-day surgical complication rate after RP. Simulated annealing (SA) was used to develop a novel co-morbidity index. Finally, the newly identified groups of co-morbid conditions were compared with the CCI and Klabunde indexes. RESULTS: Our SA identified 10 and 7 individual co-morbid conditions able to predict 90-day medical and surgical complications respectively. This novel model showed improved predictive accuracy over CCI and KCI for the 2 endpoints considered (respectively: 59.4 vs. 58.1 and 58.0% for medical complications, 58.0 vs. 56.8 and 56.7% for surgical complications). CONCLUSIONS: The newly defined groupings of co-morbid conditions resulted in better ability to predict the 2 endpoints of interest compared to CCI and KCI. However, the gain was marginal. This implies that better tools should be defined to more accurately predict these outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".