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Enregistrement W3028237932 · doi:10.7939/r3-hxjr-jm18

Can screening on admission identify children who are malnourished?

2019· article· en· W3028237932 sur OpenAlexaboutno aff
Laura Carter

Notice bibliographique

RevueUniversity of Alberta Library · 2019
Typearticle
Langueen
DomaineNursing
ThématiqueChild Nutrition and Water Access
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineEnvironmental healthPediatrics

Résumé

récupéré en direct d'OpenAlex

Background: Children are at high risk for malnutrition during hospital admission. Over half of children admitted to hospital will exhibit signs of nutrition deterioration such as weight loss. Screening for malnutrition is a critical step in the nutrition care process, however there is currently a lack of validated screening tools specifically for children in Alberta. Multiple screening tools have been proposed for pediatric use globally, but none are validated in a Canadian population. There is insufficient information available to select one screening tool over the others for widespread use. A rigorous process of selection and validation is required to determine which of the available pediatric nutrition screening tools is appropriate for use in a specific population.Objectives: The aim of this paper is to first, compare available pediatric nutrition screening tools and select two appropriate for use in an Alberta institute. The second aim is to compare the two selected tools in a validation study at the Stollery Children’s Hospital in Edmonton, Alberta, and propose one appropriate for implementation into clinical care.Methods: A literature review identified five nutrition screening tools created and validated for use in children admitted to hospital. Of those five tools, two were believed to be appropriate for use in Alberta; the Pediatric Nutrition Screening Tool (PNST), and the Screening Tool for Risk on Nutritional Status and Growth (STRONGkids). These two tools were evaluated to determine which was best able to identify malnutrition risk on admission with acceptable sensitivity, specificity, and agreement with the Subjective Global Nutritional Assessment (SGNA). Patients admitted to surgery and medicine units at an Alberta pediatric hospital were approached to participate (n=165). Both screening tools were completed on each patient by a nurse and a nutrition risk score was calculated basediiion recommended cut-offs. The SGNA was then completed by a trained dietitian, blinded to the results of the screen. Statistics: Sensitivity and specificity were calculated for both screening tool against the SGNA. A Receiver Operator Characteristic (ROC) curve was used to assess alternate cut-offs for each tool. Results: Based on the SGNA, 29% of patients were malnourished on admission. Using the recommended cut-offs STRONGkids identified 56% and 16% as at moderate and severe nutrition risk respectively with a sensitivity of 89%, specificity of 35%, and Cohen’s K of 0.483. PNST identified 26% as at nutrition risk with a sensitivity of 58%, specificity of 88%, and Cohen’s K of 0.601. Using adjusted cut-offs based on ROC curve analysis, the PNST improved to a sensitivity of 87%, specificity of 71%, and Cohen’s K of 0.681, and STRONGkids improved to a sensitivity of 80%, specificity of 61%, and Cohen’s K of 0.5. Those who were malnourished based on the SGNA stayed in hospital 2.9 days longer than those well-nourished (p < 0.05). Children identified as at nutrition risk by both tools using original and adjusted cut-offs had significantly longer lengths of hospital stay.Conclusion: This study showed neither tool was able to identify children at nutrition risk with acceptable concurrent validity in this population. When the nutrition risk cut-offs were adjusted to better fit the study population, both tools had better agreement with the SGNA. The PNST with adjusted cut-offs had the strongest concurrent validity and appears to be the tool best suited for use in Alberta pediatric hospitals. Selection of a nutrition screening tool is the first step in creation of pediatric nutrition care algorithm to guide clinicians and positively impact the nutrition care of children while admitted to hospital.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,106
Score d'incertitude au seuil0,211

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0030,025
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,003
Études des sciences et des technologies0,0000,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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 GPT0,200
Écart entre enseignants0,193 · 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 source (Gemma direct ou Codex distillé), 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

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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