30 Predicting gestational age improves newborn screening for congenital adrenal hyperplasia
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».