Unlocking the Potential Efficacy and Tolerability of Low Glycemic Index Therapy in Drug-Resistant Epilepsy among Children: Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
BACKGROUND: Drug-resistant epilepsy challenges clinical management, with many patients failing to find relief. Low Glycemic Index Therapy (LGIT) shows promise but lacks clear efficacy data. Clarifying LGIT's effectiveness could ease patient burden and improve seizure management. AIM: To evaluate the efficacy, tolerability, and adverse effects of Low Glycemic Index Therapy (LGIT) as an adjunctive treatment for drug-resistant epilepsy, utilizing a meta-analysis approach. METHODS: We followed a meticulous approach to conducting a meta-analysis on Low Glycemic Index Therapy (LGIT) in drug-resistant epilepsy, leveraging databases such as PubMed, Embase, Scopus, and the Cochrane Library. A total of twelve studies meeting inclusion criteria were identified. Comprehensive search terms and filters were applied to retrieve relevant data. Two independent reviewers meticulously screened titles, abstracts, and full texts, ensuring adherence to predefined criteria. Data extraction encompassed study characteristics, participant demographics, intervention details, and outcomes, including seizure frequency, %reduction, and adverse events. Quality assessment utilized established tools like the Cochrane Risk of Bias tool and the Newcastle-Ottawa Scale. Statistical analyses incorporated mean differences, risk ratios, and sensitivity/subgroup analyses. Ethical considerations were upheld, and reporting followed PRISMA guidelines. Limitations, including potential biases and heterogeneity, were acknowledged, with sensitivity analyses conducted to enhance findings' validity. This systematic methodology ensures a comprehensive evaluation of LGIT's efficacy, tolerability, and adverse effects in drug-resistant epilepsy patients. RESULTS: Variations in adverse effects and compliance further highlight heterogeneous responses to LGIT. Despite promising results, limitations include study design variability and short follow-up durations, potentially affecting generalizability and long-term outcomes assessment. Mean and risk differences across twelve studies investigating Low Glycemic Index Therapy (LGIT) in drug-resistant epilepsy showed significant reductions in seizure frequency were observed with LGIT compared to control groups (mean difference: -1.97 [-3.48, -0.47], Z = 2.56, p = 0.01). Heterogeneity analysis revealed substantial variability (Tau² = 2.04, Chi² = 49.89, df = 3, I² = 86%). Funnel plots further underscored LGIT's efficacy, with a mean difference of 4.80 [1.98, 7.61] favoring experimental interventions. However, heterogeneity remained considerable (Tau² = 6.20, Chi² = 7.14, df = 4, I² = 63%). Risk differences favored LGIT but were not statistically significant (total: -0.11 [-0.35, 0.13], Z = 0.89, p = 0.37). CONCLUSIONS: Diverse study designs and participant cohorts provide insights into LGIT's efficacy, tolerability, and adverse effects. Notably, LGIT consistently reduces seizure frequency across studies, as evidenced by significant results in multiple investigations.
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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 | 0,008 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| 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 ».