The pediatric glucocorticoid toxicity index
Notice bibliographique
Résumé
To develop a Pediatric glucocorticoid toxicity index (pGTI), a standardized, weighted clinical outcome assessment that measures change in glucocorticoid (GC) toxicity over time. Fourteen physician experts from 7 subspecialties participated. The physician experts represented multiple subspecialties in which GCs play a major role in the treatment of inflammatory disease: nephrology, rheumatology, oncology, endocrinology, genetics, psychiatry, and maternal-fetal medicine. Nine investigators were from Canada, Europe, or New Zealand, and 5 were from the United States. Group consensus methods and multi-criteria decision analysis were used. The pGTI is an aggregate assessment of GC toxicities that are common, important, and dynamic. These toxicities are organized into health domains graded as minor, moderate, or major and are weighted according to severity. The relative weights were derived by group consensus and multi-criteria decision analysis using the 1000MindsTM software platform. Two quantitative scores comprise the overall toxicity profile derived from pGTI data: (1) the Cumulative Worsening Score; and (2) the Aggregate Improvement Score. The pGTI also includes a qualitative, unweighted record of GC side-effects known as the Damage Checklist, which documents less common toxicities that, although potentially severe, are unlikely to change with varying GC dosing. One hundred and seven (107) toxicity items were included in the pGTI and thirty-two (32) in the Damage Checklist. To assess the degree to which the pGTI corresponds to expert clinical judgement, the investigators ranked 15 cases by clinical judgement from highest to lowest GC toxicity. Expert rankings were then compared to case ranking by the pGTI, yielding excellent agreement (weighted kappa 0.86). The pGTI was migrated to a digital environment following its development and initial validation. The digital platform is designed to ensure ease-of-use in the clinic, rigor in application, and accuracy of scoring. Clinic staff enter vital signs, laboratory results, and medication changes relevant to pGTI scoring. Clinicians record findings for GC myopathy, skin toxicity, mood dysfunction, and infection. The pGTI algorithms then apply the weights to these raw data and calculate scores. Embedded logic accounts for the impact of age- and sex-related reference ranges on several health domains: blood pressure, lipid metabolism, and bone mineral density. Other algorithms account for anticipated changes in the height Z-scores used in the growth domain, thereby addressing a concern unique to GC toxicity in children. The Damage Checklist ensures comprehensive measurement of GC toxicity but does not contribute to pGTI scoring, because the scored domains emphasize manifestations of GC toxicity that are likely to change over the course of a trial. We describe the development and initial evaluation of a weighted, composite toxicity index for the assessment of morbidity related to GC use in children and adolescents. Developing the pGTI digital platform was essential for performing the nuanced calculations necessary to ensure rigor, accuracy, and ease-of-use in both clinic and research settings.
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 | 0,000 |
| 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 | 0,000 | 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 ».