Understanding the Natural Progression of Spina Bifida: Prospective Study
Notice bibliographique
Résumé
BACKGROUND: Spina bifida (SB) is monitored through birth defects surveillance across the United States and in most developed countries. Although much is known about the management of SB and its many comorbid conditions in affected individuals, there are few systematic, longitudinal studies on population-based cohorts of children or adults. The natural history of SB across the life course of persons with this condition is not well documented. Earlier identification of comorbidities and secondary conditions could allow for earlier intervention that might enhance the developmental trajectory for children with SB. OBJECTIVE: The purpose of this project was to assess the development, health, and condition progression by prospectively studying children who were born with SB in Arizona and Utah. In addition, the methodology used to collect the data would be evaluated and revised as appropriate. METHODS: Parents of children with SB aged 3-6 years were eligible to participate in the study, in English or Spanish. The actual recruitment process was closely documented. Data on medical history were collected from medical records; family functioning, child behaviors, self-care, mobility and functioning, and health and well-being from parent reports; and neuropsychological data from testing of the child. RESULTS: In total, 152 individuals with SB were identified as eligible and their parents were contacted by site personnel for enrollment in the study. Of those, 45 (29.6%) declined to participate and 6 (3.9%) consented but did not follow through. Among 101 parents willing to participate, 81 (80.2%) completed the full protocol and 20 (19.8%) completed the partial protocol. Utah enrolled 72.3% (73/101) of participants, predominately non-Hispanic (60/73, 82%) and male (47/73, 64%). Arizona enrolled 56% (28/50) of participants they had permission to contact, predominately Hispanic (18/28, 64%) and male (16/28, 57%). CONCLUSIONS: We observed variance by site for recruitment, due to differences in identification and ascertainment of eligible cases and the required institutional review board processes. Restriction in recruitment and the proportion of minorities likely impacted participation rates in Arizona more than Utah.
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 | 0,002 | 0,000 |
| 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 | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».