Improved Performance of Newborn Screening for Congenital Adrenal Hyperplasia Using 21-deoxycortisol Measurement
Bibliographic record
Abstract
Purpose: Newborn screening for 21-hydroxylase deficiency congenital adrenal hyperplasia (CAH) has a high false-positive rate. A second-tier steroid profile using liquid chromatography mass spectrometry can improve specificity. Multiple screening algorithms were evaluated to optimize the performance of screening for salt-wasting CAH (SW-CAH). Methods: Principal components analysis guided potential combinations of steroid biomarkers for evaluation in a study population of 1710 immunoassay-positive samples proceeding to the second-tier steroid profile in the Newborn Screening Ontario program between August 2020 and April 2023. A Monte Carlo simulation was used to evaluate the performance of algorithms and cutoffs. Results: Optimal performance for the identification of SW-CAH used a 3-component second-tier algorithm: detectable 21-deoxycortisol (≥ 2.1 nmol/L); 17-hydroxyprogesterone + 21-deoxycortisol ≥ 40 nmol/L; and ratio of (17-hydroxyprogesterone + 21-deoxycortisol)/cortisol ≥ 0.3. All 8 cases of SW-CAH were accurately identified with a positive predictive value of 70% and 100% sensitivity for SW-CAH, whereas 1 known case of simple virilizing (SV) CAH screened negative. When applied to 26 historical cases, the algorithm identified all 13 cases of SW-CAH and all 6 SV-CAH cases, whereas other forms of CAH were filtered out because of low 21-deoxycortisol. Conclusion: Using 21-deoxycortisol for second-tier screening and applying a 3-component algorithm can improve performance of newborn screening for SW-CAH, reducing burden on patients and the health care system. Although cases of SV-CAH may be identified, the thresholds were set to identify life-threatening SW-CAH with a high positive predictive value and 100% sensitivity.
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 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".