Automated telephone reminder messages can assist electronic diabetes care
Bibliographic record
Abstract
Telephone reminder systems have been used to assist in the treatment of many chronic diseases. However, it is unclear if these systems can increase medication and appointment adherence in patients with diabetes without direct patient-provider telephone contact. We tested the feasibility of using an automated telephone reminder system (ATRS) to deliver reminder messages to 253 adults with diabetes enrolled in a randomized controlled trial. Eighty-four percent of the patients were able to register using voice recognition and at least one reminder was delivered to 95% of registered patients over a period of 7.5 months. None of the demographic features studied predicted a patient's ability to enroll or to receive reminder calls. At the end of the study, 63% of patients indicated that they wished to continue to receive ATRS calls. The level of system use as determined by the number of received reminder calls was not associated with a change in the number of physician visits or diabetes-related laboratory tests during follow-up. The clinical benefits and sustainability of ATRS remain unproven, but our results indicate that an automated reminder system can be effective for providing messages to a large group of older patients with diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".