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Enregistrement W4290659509 · doi:10.1001/jamapediatrics.2022.2758

School Readiness Among Children Born Preterm in Manitoba, Canada

2022· article· en· W4290659509 sur OpenAlexaffabout
Deepak Louis, Sapna Oberoi, M. Florencia Ricci, Christy Pylypjuk, Ruben Alvaro, Mary Seshia, Cecilia de Cabo, Diane Moddemann, Lisa M. Lix, Allan Garland, Chelsea Ruth

Notice bibliographique

RevueJAMA Pediatrics · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueInfant Development and Preterm Care
Établissements canadiensManitoba HealthUniversity of Manitoba
Organismes subventionnairesnon disponible
Mots-clésMedicineCohortPediatricsPopulationLogistic regressionGestational ageDemographyCohort studyPregnancyEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Importance: Children born preterm may experience learning challenges at school. However, there is a paucity of data on the school readiness of these children as they prepare to begin grade 1. Objective: To examine the association between prematurity and school readiness in a population-based cohort of children. Design, Setting, and Participants: This cohort study was conducted in the province of Manitoba, Canada, and involved 2 cohorts of children in kindergarten at the time of data collection. The population-based cohort included children born between January 1, 2000, and December 31, 2011, whose school readiness was assessed in kindergarten using the Early Development Instrument (EDI) data. The sibling cohort comprised children born preterm and their closest-in-age siblings born full term. Data were analyzed between March 12 and September 28, 2021. Exposures: Preterm birth, defined as gestational age (GA) less than 37 weeks. Main Outcomes and Measures: The primary outcome was vulnerability in the EDI, defined as a score below the tenth percentile of the Canadian population norms for any 1 or more of the 5 EDI domains (physical health and well-being, social competence, emotional maturity, language and cognitive development, and communication skills and general knowledge). Logistic regression models were used to identify the factors associated with vulnerability in the EDI. P values were adjusted for multiplicity using the Simes false discovery method. Results: Of 86 829 eligible children, 63 277 were included, of whom 4352 were preterm (mean [SD] GA, 34 [2] weeks; 2315 boys [53%]) and 58 925 were full term (mean [SD] GA, 39 (1) weeks; 29 885 boys [51%]). Overall, 35% of children (1536 of 4352) born preterm were vulnerable in the EDI compared with 28% of children (16 449 of 58 925) born full term (adjusted odds ratio [AOR], 1.32; 95% CI, 1.23-1.41; P < .001]). Compared with children born full term, those born preterm had a higher percentage of vulnerability in each of the 5 EDI domains. In the population-based cohort, prematurity (34-36 weeks' GA: AOR, 1.23 [95% CI, 1.14-1.33]; <34 weeks' GA: AOR, 1.72 [95% CI, 1.48-1.99]), male sex (AOR, 2.24; 95% CI, 2.16-2.33), small for gestational age (AOR, 1.31; 95% CI, 1.23-1.40), and various maternal medical and sociodemographic factors were associated with EDI vulnerability. In the sibling cohort, EDI outcomes were similar for both children born preterm and their siblings born full term except for the communication skills and general knowledge domain (AOR, 1.39; 95% CI, 1.07-1.80) and Multiple Challenge Index (AOR, 1.43; 95% CI, 1.06-1.92), whereas male sex (AOR, 2.19; 95% CI, 1.62-2.96) and maternal age at delivery (AOR, 1.53; 95% CI, 1.38-1.70) were associated with EDI vulnerability. Conclusions and Relevance: Results of this study suggest that, in a population-based cohort, children born preterm had a lower school-readiness rate than children born full term, but this difference was not observed in the sibling cohort. Child and maternal factors were associated with lack of school readiness among this population-based cohort.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil0,821

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,007
Tête enseignante GPT0,201
Écart entre enseignants0,194 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations17
Publié2022
Routes d'admission2
Résumé présentoui

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