External Validation of Clinical Prediction Models in Unilateral Primary Aldosteronism
Bibliographic record
Abstract
BACKGROUND: Targeted treatment of primary aldosteronism (PA) is informed by adrenal vein sampling (AVS), which remains limited to specialized centers. Clinical prediction models have been developed to help select patients who would most likely benefit from AVS. Our aim was to assess the performance of these models for PA subtyping. METHODS: This external validation study evaluated consecutive patients referred for PA who underwent AVS at a tertiary care referral center in Alberta, Canada during 2006-2018. In alignment with the original study designs and intended uses of the clinical prediction models, the primary outcome was the presence of lateralization on AVS. Model discrimination was evaluated using the C-statistic. Model calibration was assessed by comparing the observed vs. predicted probability of lateralization in the external validation cohort. RESULTS: The validation cohort included 342 PA patients who underwent AVS (mean age, 52.1 years [SD, 11.5]; 201 [58.8%] male; 186 [54.4%] with lateralization). Six published models were assessed. All models demonstrated low-to-moderate discrimination in the validation set (C-statistics; range, 0.60-0.72), representing a marked decrease compared with the derivation sets (range, 0.80-0.87). Comparison of observed and predicted probabilities of unilateral PA revealed significant miscalibration. Calibration-in-the-large for every model was >0 (range, 0.35-1.67), signifying systematic underprediction of lateralizing disease. Calibration slopes were consistently <1 (range, 0.35-0.87), indicating poor performance at the extremes of risk. CONCLUSIONS: Overall, clinical prediction models did not accurately predict AVS lateralization in this large cohort. These models cannot be reliably used to inform the decision to pursue AVS for most 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 | 0.091 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".