30 Predicting gestational age improves newborn screening for congenital adrenal hyperplasia
Bibliographic record
Abstract
Abstract Background Newborn screening for congenital adrenal hyperplasia is performed using a two-tier approach. The first tier involves comparison of neonate 17-hydroxyprogesterone levels to gestational age (GA)-based thresholds. When GA is unreported, which occurs in approximately 5% of births, birth weight (BW)-based thresholds are the only available option. However, these have a lower specificity and result in more false positive results. Recently, a predictive model was developed to estimate GA based on newborn demographics and the screening analytes measured in a blood sample. Objectives The objective of this study was to determine whether supplying a predicted GA to newborns with unreported GA, and subsequent GA-based screening, has a higher positive predictive value than BW-based screening. Design/Methods Screening data was obtained for approximately 700,000 births that occurred in Canada between 2011 and 2015. Predicted GA was calculated using a model composed of demographic and screening analyte factors. The positive predictive values of BW- and predicted GA-based screening were calculated for newborns with unreported GA. A sequential approach was then developed whereby newborns with unreported GA were first screened by BW-based screening. Newborns that screened positive were then supplied with their predicted GA and screened using GA-based thresholds. Results First-tier CAH screening using GA-based 17-hydroxyprogesterone thresholds had a higher positive predictive value than using BW-based thresholds (1.30% vs. 0.82%). In the study time period, 3.61% of newborns had an unreported GA. For these newborns, predicted GA-based screening had a higher positive predictive value than BW-based screening (0.83% vs. 0.76%) and correctly identified the 2 infants with CAH whose GA was unreported. A sequential screening approach was then used: BW-based screening and, for the screen positive population, predicted GA-based screening. This further increased the positive predictive value compared to BW-based screening (0.95% vs. 0.76%), reduced the false positive rate, and correctly identified true positive cases. Conclusion Reducing the false positive rate of CAH screening is important to prevent unnecessary second-tier screening and referrals. For newborns with unreported GA (4-5% of all births), BW-based screening is the only currently available approach. However, this approach has a poor specificity and a high false positive rate compared to GA-based screening. This study is the first to demonstrate an alternative screening strategy with a higher positive predictive value for newborns with unreported GA.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".