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Enregistrement W1837091423 · doi:10.7322/jhgd.50414

IMPACT OF BIRTH-WEIGHT ON ADULT MINOR ILLNESS

2013· article· en· W1837091423 sur OpenAlexaff
Amit Mukerji, Jaques Belik

Notice bibliographique

RevueJournal of Human Growth and Development · 2013
Typearticle
Langueen
DomaineMedicine
ThématiqueBirth, Development, and Health
Établissements canadiensUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésMinor (academic)PsychologyLow birth weightPediatricsMedicinePregnancyArtHumanitiesBiology

Résumé

récupéré en direct d'OpenAlex

There is now well-established evidence that low birthweight has important health implications beyond infancy and childhood as suggested by the fetal origins of disease theory1. It postulates that in utero under-nutrition leads to permanent changes to the physiology and metabolism of the body, in part explaining the higher incidence of cardiovascular illnesses,2,3 stroke,4 and type 2 diabetes5 in that population. In addition, lower birthweight is associated with higher mortality rates from all causes6 . An area where the evidence is still in early stages is that of the link between birthweight and adult minor illnesses. These conditions include common cold and viral respiratory tract infections, headache and gastrointestinal disturbances and account for between 18-40% of the general practitioner’s time7,8. Minor illnesses also have significant economic impact. They were estimated to cost the UK’s National Health Service (NHS) $2.2 billion per year7 and lead to significant workabsenteeism9 . Such disease-related economic impact led not only to an emphasis on promoting self-care measures for minor ailments,10 but also an attempt to better understand its epidemiology. Previous work by Belingham-Young11 introduced the notion that birthweight may be related to adult minor illness. Until now, such notion only garnered limited attention and the current study by the same group filled this important knowledge gap12. In this cross-sectional retrospective cohort study, the authors used a minor illness checklist completed by 258 participants (219 female, 39 male) who identify themselves as having been born at term and knew their birthweight. A median split of the total scores was used to divide the participants into low and high minor illness groups. They were also grouped based on optimal (3,500 – 4,500 grams) and suboptimal birthweight (2540 – 3490 grams). Interestingly, minor illness scores were significantly lower for those in the optimal birthweight, and there was a significant negative correlation between birthweight and minor illness score. The authors argue that their findings have significant public health implications. Health care prevention initiatives favoring individuals of suboptimal birth weights may have a positive impact on the frequency and severity of minor infection-related illnesses. As suggested by the authors, targeting influenza vaccinations towards this high risk group may be cost-effective in terms of preventing complications associated with this infection. However, they also address some of the practical challenges of broad implementation of health policies based on birthweight, as such data is often limited to the patients’ chart. An Equilibrium Model is discussed that may help public health practitioners in identifying and prioritizing local implementation. The results of this study bear particular public health importance as there is tremendous focus on curbing rising health care costs, especially as many parts of the world are faced with an increasingly aging population13. Yet, there are a few points worth considering. Although in part Journal of Human Growth and Development 2013; 23(1): 7-10 EDITORIAL

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 enseignants0,045
Score d'incertitude au seuil0,509

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,018
Tête enseignante GPT0,289
Écart entre enseignants0,271 · 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

Citations3
Publié2013
Routes d'admission1
Résumé présentoui

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