Adrenal vein sampling: External validation of multinomial regression modelling and left adrenal vein‐to‐peripheral vein ratio to predict lateralization index without right adrenal vein sampling
Bibliographic record
Abstract
BACKGROUND: Adrenal vein sampling (AVS) failure is mainly due to right adrenal vein unavailability. Multinomial regression modelling (MRM) and left adrenal vein-to-peripheral vein ratio (LAV/PV) were proposed to predict the lateralization index without the right AVS. OBJECTIVE: To assess external validity of MRM and LAV/PV to predict lateralization index when right adrenal vein sampling is missing. DESIGN: Diagnostic retrospective study. PATIENTS: Development and validation cohorts included AVS of 174 and 122 patients, respectively, from 2 different centres. MEASUREMENTS: Development and validation cohort data were used, respectively, for calibration and for validation of MRM and LAV/PV to predict the lateralization index without the right adrenal vein sampling. Sensitivity and specificity of MRM and LAV/PV were compared between both centres at different pre-established specificity thresholds based on receiver operating characteristic curves generated from the development cohort data. RESULTS: At a specificity threshold of 95% set in the development cohort, specificity values exceeded 90% (range, 90.6%-98.8%) for all verified MRM and LAV/PV models in the validation cohort. Corresponding sensitivities for MRM and LAV/PV, respectively, range from 54.1% to 83.7% and 32.8% to 88.4% for the development cohort compared to 33.3%-87.5% and 2.8%-79.2% for the validation cohort. Overall, diagnostic accuracy of both methods was higher to detect right (82.8%-93.5%) than left (70.2%-80.6%) lateralization index status in both centres. CONCLUSIONS: Minimal changes in specificity from development to validation cohorts validate the use of MRM and LAV/PV to predict the lateralization index when the right AVS is missing. Both methods had better accuracy for right than left lateralization detection.
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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 | 0.052 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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".