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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".