Telemedicine Consultations: An Alternative Model to Increase Access to Diabetes Specialist Care in Underserved Rural Communities
Bibliographic record
Abstract
BACKGROUND: Diabetes care in rural communities often suffers because of physician shortages. When patients need to see an endocrinologist, long-distance travel to urban centers can constitute a barrier to care. OBJECTIVE: To address this problem, we tested whether diabetes telemedicine consultations would be acceptable to rural patients and their primary care providers as an alternative care model. METHODS: Twenty-five patients with diabetes in a rural, medically underserved community received glycemic management recommendations via videoconferencing-based teleconsultation with an endocrinologist at an urban center. At the rural site, a nurse trained in diabetes care assisted with the visits. Outcomes measured were patient and primary care provider satisfaction (measured by structured questionnaires) and glycosylated hemoglobin (HbA1c) levels. RESULTS: Patients and providers uniformly reported high levels of satisfaction and acceptability. Mean HbA1c decreased from 9.6% to 8.5% (P < .001). CONCLUSIONS: Teleconsultations are well accepted by users (patients and primary care physicians) and glycemic control seems to improve in patients with diabetes. This new model of care could potentially expand access to specialist care in isolated rural communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".