A Systematic Review of Telehealth Applications in Endocrinology
Bibliographic record
Abstract
Introduction: The prevalence of telehealth has witnessed a significant increase in various medical domains, especially in endocrinology. Telehealth brings about considerable advantages for both patients and health care professionals. However, despite these positive aspects, the growing prominence of telehealth is accompanied by certain challenges. This systematic review aims to assess the role of telehealth in endocrinology, including its applications, effectiveness, challenges, and implications for patient care. Methods: This study involved a thorough search using comprehensive techniques across databases such as PubMed/Medline, Embase, and Scopus. The studies were selected for a tailored adaptation of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to enhance the clarity of our systematic review's reporting. Results: This systematic review explores global telemedicine applications in endocrinology. Addressing various endocrine conditions, interventions utilize technology tools such as smartphones and applications, offering multifaceted utility from education and data gathering to screening and treatment. Notably, these interventions demonstrate adaptability during the COVID-19 pandemic. Positive outcomes include enhanced patient education, disease self-management, reduced complications, and improved glycemic control. However, drawbacks include the need for technical proficiency, perceived lower care quality, and potential privacy risks. These nuanced findings contribute to the discourse on telemedicine efficacy and limitations. Conclusion: In conclusion, telehealth holds significant potential in transforming endocrine care. While there are challenges to its implementation, the benefits it offers underscore its value as a health care delivery model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".