Advances in congenital adrenal hyperplasia newborn screening: 11-ketotestosterone and 21-deoxycortisone as additional discriminatory biomarkers
Bibliographic record
Abstract
BACKGROUND: A major limitation of newborn screening (NBS) for congenital adrenal hyperplasia (CAH) is the lack of specificity of the fluoroimmunoassay (FIA) currently used for 17-hydroxyprogesterone (17OHP) determination. This issue is more pronounced in newborns, due to elevated levels of interfering compounds. Fluoroimmunoassay at our NBS centre in Ile-de-France has a false-positive rate of around 80% and a predictive positive value of 16% for first- and second-tier measurements from dried blood spots. Recently, tandem mass spectrometry (LC-MS/MS) has gained international recognition as a complementary tool to FIA testing. Currently, the most frequently used biomarkers are 17OHP and 21-deoxycortisol, used either alone or in combination with steroid ratios such as cortisol or 4-androstenedione. Concurrently, the class of 11-oxygenate-androgens-such as 11-ketotestosterone-and more recently 11-oxygenate pregnanes-such as 21-deoxycortisone-has attracted growing interest in the diagnosis of 21-hydroxylase deficiency. These derivatives result from the combined action of 11-beta hydroxysteroid dehydrogenase and 11-beta hydroxylase. METHODS: We propose a revisited LC-MS/MS steroid profile, enriched with these classes of biomarkers, to be included in the CAH NBS algorithm. This combination could be used as a multi-steroid approach implemented using a machine learning model. RESULTS: Our preliminary results suggest that these oxygenated androgen/pregnane steroids are significantly discriminative to streamline the NBS process for CAH. We have demonstrated this in 2 different NBS centres, in the greater Paris region and in Brittany, France. CONCLUSION: This new algorithm could have an important impact on reducing the number of recall and family stress related to NBS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".