Impact of Telemedicine Versus In-Person Pediatric Outpatient Type 1 Diabetes Visits on Immediate Glycemic Control: Retrospective Chart Review
Notice bibliographique
Résumé
BACKGROUND Children, adolescents, and young adults with type 1 diabetes require frequent outpatient evaluation to assess glucose trends, modify insulin doses, and screen for complications. Continuous glucose monitoring (CGM) has become more prevalent in recent years and provides a detailed glycemic control assessment. Telemedicine has been increasingly utilized in lieu of in-person visits, especially since the onset of the COVID-19 pandemic. OBJECTIVE To investigate whether CGM profile parameters change in the periods immediately following an outpatient type 1 diabetes follow-up visit and to determine whether visits completed by telemedicine versus in-person impact these metrics, screening lab completion rates, or frequency of diabetic emergencies. METHODS A dual center retrospective chart review was conducted including outpatient type 1 diabetes follow-up visits for patients aged 5-21 years old utilizing Dexcom CGM during the 2021 calendar year. CGM profile change from baseline was assessed via Time in Range (TIR) and Glucose Management Indicator (GMI) comparing the 4-week period leading up to each outpatient visit to the 2-week and 4-week period immediately following. Completion of recommended screening labs collected prior to the next visit was determined. Diabetic emergencies were measured by frequency of glucagon use and/or diabetes-related ER visits or hospitalizations. RESULTS A total of 269 outpatient visits (41.2% telemedicine) among 135 patients were included across both study sites. Mean TIR increased by 1.63% and 1.35% during the 2-week and 4-week period after each visit, respectively (p = 0.003, 0.013). Mean GMI also improved, decreasing by 0.07% and 0.06% during the 2-week and 4-week period after each visit, respectively (p = 0.003, 0.021). There were no statistically relevant differences in TIR and GMI changes when comparing telemedicine versus in-person visits. However, patients seen by in-person visits were 2.69 times more likely to complete screening labs than those seen by telemedicine (p = 0.032). Diabetic emergencies occurred too infrequently to analyze. CONCLUSIONS Our study detected a small, but statistically significant improvement in CGM profile parameters that was similar between telemedicine and in-person follow-up visits. However, screening lab completion occurred more often in patients seen in-person, highlighting the importance of periodic face-to-face encounters. Since multiple visits over time are needed to sustain clinically relevant improvements in glycemic control, our findings suggest that telemedicine can be utilized to enhance diabetes care among children and adolescents. Future studies should examine how to best utilize a combination of telemedicine versus in-person visits to achieve diabetes care goals while providing flexibility for patients.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».