Development and internal validation of a predictive score for the diagnosis of central adrenal insufficiency when morning cortisol is in the grey zone
Bibliographic record
Abstract
BACKGROUND: When evaluating a patient for central adrenal insufficiency (CAI), there is a wide range of morning cortisol values for which no definite conclusion on hypothalamus-pituitary-adrenal (HPA) axis function can be drawn; in these cases, a stimulation test is required. Aim of this study was to develop an integrated model for CAI prediction when morning cortisol is in the grey zone, here defined as 40.0-160.0 μg/L. METHODS: Overall, 119 patients with history of sellar tumour which underwent insulin tolerance test (ITT) for the evaluation of HPA axis were enrolled. Supervised regression techniques were used for model development. RESULTS: An integrated predictive model was developed and internally validated, and showed a significantly better diagnostic performance than morning cortisol alone (AUC 0.811 vs 0.699, p = 0.003). A novel predictive score (CAI-score) was retrieved, on a 5.5-point scale, by considering morning cortisol (0 points if 130.1-160.0 μg/L, 1 point if 100.1-130.0 μg/L, 1.5 points if 70.1-100.0 μg/L, 2.5 points if 40.0-70.0 μg/L), other pituitary deficits (2 points if ≥ 3 deficits), and sex (1 point if male). A diagnostic algorithm integrating CAI-score and ITT was finally proposed, with an overall accuracy of 99%, and the possibility to avoid the execution of stimulation tests in 25% of patients. CONCLUSIONS: This was the first study that proposed an integrated score for the prediction of CAI when morning cortisol is in the grey zone. This score might be helpful to reduce the number of patients who need a stimulation test for the assessment of HPA axis function.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".