76 Widening disease definitions in gestational diabetes: an evaluation of changing guidelines
Notice bibliographique
Résumé
<h3>Objectives</h3> The incidence of gestational diabetes mellitus (GDM) is rapidly increasing worldwide, raising a concern of overdiagnosis. While population trends such as obesity, increased age at motherhood, and ethnic changes play a role in this increase, another major cause is the widening of the diagnostic criteria. The primary aim of this study is to evaluate what factors were taken into consideration when new diagnostic criteria for GDM were made. The Guidelines International Network (G-I-N) Preventing Overdiagnosis workgroup recently developed guidance for modifying the definition of disease, including a checklist for items to consider when widening disease definitions.<sup>1</sup> The secondary aim of this study was to pilot the use of this G-I-N checklist. <h3>Method</h3> Documents in which currently used criteria for gestational diabetes were proposed were the focus of this study. Changes to thresholds, timing of testing, and the combination of abnormal test results required were considered changes to definitions, but changes to screening strategies were considered outside the scope. These definition documents were found by backward reference searching from 5 recent reviews of gestational diabetes and through searching of websites of professional and guideline organizations. Documents containing new definitions were assessed against the 8-item G-I-N checklist to evaluate what domains were considered when proposing a new disease definition. <h3>Results</h3> We identified 14 documents which proposed modifying the diagnosis of GDM. Four types of definitions were observed: a percentile definition similar to laboratory reference ranges (n=4); harmonization with type two diabetes mellitus (n=6); a risk-based assessment examining the risk of maternal and fetal adverse outcomes (n=3); and one informed by a health technology assessment (n=1). None of the 14 documents considered all 8 criteria in the G-I-N checklist. All described the new definition in detail and all but one described the trigger. None estimated the impact on the prevalence of GDM. The prognostic ability of the definitions was only assessed by risk-based criteria (n=3) and little attention was given to accuracy, repeatability, or reproducibility (n=2). Potential benefits were mentioned by half (n=7) and harms by fewer (n=4). The balance between harms and benefits was only discussed by 3. <h3>Conclusions</h3> Our analysis of the changes to criteria for GDM reveals a complex history of definitions stemming from 4 conceptual bases. There appears to be a paucity of primary research data used in the development of definitions for GDM. While harms and benefits of changing the definition were sometimes mentioned, there was no explicit consideration or quantification of the benefits versus harms, making thresholds chosen appear arbitrary. Given the impact of seemingly modest changes to disease definitions have on the incidence of disease, we consider definitional changes to be a substantial task for guidelines, one that requires a separate panel. Such panels should use G-I-N’s published 8-item checklist of elements to consider when modifying definitions. For key items, rapid systematic reviews should be considered. Finally, panels could be more cautious in applying dichotomous disease labels and instead use a stratified terminology that reflects a spectrum of risk. <h3>REFERENCE</h3> 1. Doust J, Vandvik PO, Qaseem A, et al. Guidance for modifying the definition of diseases a checklist. <i>JAMA Intern Med</i> 2017;<b>177</b>(7):1020–1025. doi:10.1001/jamainternmed.2017.1302
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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) | 0,001 | 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 ».