Mobile health applications for self-regulation of glucose levels in type 2 diabetes mellitus patients: a systematic review
Bibliographic record
Abstract
AMA Syabariyah S, Wardani P, Aisyah P, Hisan U. Mobile health applications for self-regulation of glucose levels in type 2 diabetes mellitus patients: a systematic review. Family Medicine & Primary Care Review. 2024;26(1):123-136. doi:10.5114/fmpcr.2024.134712. APA Syabariyah, S., Wardani, P., Aisyah, P., & Hisan, U. (2024). Mobile health applications for self-regulation of glucose levels in type 2 diabetes mellitus patients: a systematic review. Family Medicine & Primary Care Review, 26(1), 123-136. https://doi.org/10.5114/fmpcr.2024.134712 Chicago Syabariyah, Sitti, Puput Putri Kusuma Wardani, Popy Siti Aisyah, and Urfa Khairatun Hisan. 2024. "Mobile health applications for self-regulation of glucose levels in type 2 diabetes mellitus patients: a systematic review". Family Medicine & Primary Care Review 26 (1): 123-136. doi:10.5114/fmpcr.2024.134712. Harvard Syabariyah, S., Wardani, P., Aisyah, P., and Hisan, U. (2024). Mobile health applications for self-regulation of glucose levels in type 2 diabetes mellitus patients: a systematic review. Family Medicine & Primary Care Review, 26(1), pp.123-136. https://doi.org/10.5114/fmpcr.2024.134712 MLA Syabariyah, Sitti et al. "Mobile health applications for self-regulation of glucose levels in type 2 diabetes mellitus patients: a systematic review." Family Medicine & Primary Care Review, vol. 26, no. 1, 2024, pp. 123-136. doi:10.5114/fmpcr.2024.134712. Vancouver Syabariyah S, Wardani P, Aisyah P, Hisan U. Mobile health applications for self-regulation of glucose levels in type 2 diabetes mellitus patients: a systematic review. Family Medicine & Primary Care Review. 2024;26(1):123-136. doi:10.5114/fmpcr.2024.134712.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".