Development and internal validation of a predictive model for the estimation of pheochromocytoma recurrence risk after radical surgery
Bibliographic record
Abstract
Objective: Various features have been identified as predictors of relapse after complete resection of pheochromocytoma, but a comprehensive multivariable model for recurrence risk prediction is lacking. The aim of this study was to develop and internally validate an integrated predictive model for post-surgical recurrence of pheochromocytoma. Methods: The present research retrospectively enrolled 177 patients affected by pheochromocytoma and submitted to radical surgery from 1990 to 2016, in nine referral centers for adrenal diseases. Cox regression analysis was adopted for model development, and a bootstrapping procedure was used for internal validation. Results: Variables independently associated with recurrence were tumor size (hazard ratio (HR): 1.01, 95% CI: 1.00-1.02), positive genetic testing (HR: 5.14, 95% CI: 2.10-12.55), age (HR: 0.97, 95% CI: 0.94-0.99), and Pheochromocytoma of the Adrenal Gland Scaled Score (PASS) (HR: 1.16, 95% CI: 1.04-1.29). The predictive performance of the overall model, evaluated by Somers' D, was equal to 0.594, and was significantly higher than the ones of any single predictor alone (P = 0.002 compared to tumor size; P = 0.004 compared to genetic testing; P = 0.048 compared to age; P = 0.006 compared to PASS). Internal validation by bootstrapping techniques estimated an optimistic bias of 6.3%, which reassured about a small tendency towards overfit. Conclusions: We proposed a multivariable model for the prediction of post-surgical recurrence of pheochromocytoma, derived by the integration of genetic, histopathological, and clinical data. This predictive tool may be of value for a comprehensive tailoring of post-surgical follow-up in radically operated pheochromocytoma patients.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".