Development and validation of a simple algorithm to estimate common gestational age categories using standard administrative birth record data in Ontario, Canada
Notice bibliographique
Résumé
Gestational age is often incompletely recorded in administrative records, despite being critical to paediatric and maternal health research. Several algorithms exist to estimate gestational age using administrative databases; however, many have not been validated or use complicated methods that are not readily adaptable. We developed a simple algorithm to estimate common gestational age categories from routine administrative data. We leveraged a population-based registry of all hospital births occurring in Ontario, Canada over 2002–2016 including 1.8 million birth records. In this sample, this simple algorithm had excellent performance compared to a verified measure of gestational age; 87.61% agreement (95% CI: 87.49, 87.74). The accuracy of the algorithm exceeded 98% for all of the gestational age categories. Agreement notably increased over time and was greatest among singleton births and infants born at 2500–2999 g. This study provides a straight-forward algorithm for accurately estimating common gestational age categories that is easily adaptable for use in other countries.Impact StatementWhat is already known on this subject? Gestational age is often incompletely or inaccurately recorded in administrative health databases, despite being critical to the study of many paediatric and maternal health outcomes. Consequently, researchers must rely on various methods to estimate gestational age, many of these methods are either overly simple (i.e. assuming a uniform duration) or analytically complicated and difficult to adapt for new populations (e.g. regression-based approaches).What the results of this study add? This study, based on a population-based registry of all 1.8 million births occurring in Ontario, Canada 2003–2016, found that a simple, sex-specific algorithm using three commonly recorded birth record characteristics performs almost perfectly compared to a clinical estimate recorded near birth.What the implications are of these findings for clinical practice and/or further research? This study suggests that a straight-forward, sex-specific algorithm based on routinely collected birth record data is able to accurately estimate common gestational age categories (i.e. extreme preterm, <28 weeks; very preterm, 28–32 weeks; moderate-to-late preterm, 33–26 weeks; and term, 37 weeks of completed gestational age). This work will be of greatest interest to perinatal researchers using routinely collected health administrative data.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».