Optimizing the frequency of physician encounters in follow - up care for patients with type 2 diabetes mellitus: a systematic review
Bibliographic record
Abstract
BACKGROUND: Decisions on the frequency of physician encounters for patients with type 2 diabetes mellitus (T2DM) have significant impacts on both patients' health outcomes and burden on health systems, whereas definitive intervals for physician encounters are still lacking in most clinical guidelines. This study systematically reviewed the existing evidence evaluating different frequencies of physician encounters among T2DM patients. METHODS: Systematic search of studies evaluating different visit frequencies for follow - up care in T2DM patients was performed in MEDLINE Ovid, Embase Ovid, and Cochrane library from database inception to 25 March 2022. Studies on the follow - up encounters driven by non - physicians and those on the episodic visits in the acute care settings were excluded in the screening. Citation searching was conducted via Google Scholar on the identified papers after screening. The risk of bias was assessed using Cochrane RoB2 tool for randomized controlled trials and Newcastle - Ottawa Scale for cohort studies. Findings were summarized narratively. RESULTS: Among 6363 records from the database search and 231 references from the citation search, 12 articles were eligible for in - depth review. The results showed that for patients who had not achieved cardiometabolic control, intensifying encounter frequency could enhance medication adherence, shorten the time to achieve the treatment target, and improve the patients' quality of life. However, for the patients who had already achieved the treatment targets, less frequent encounters were equivalent to intensive encounters in maintaining their cardiometabolic control, and could save considerable healthcare costs without substantially lowering the quality of care and patients' satisfaction. CONCLUSION: Existing evidence suggested that the optimal frequency of physician encounters for patients with T2DM should be individualized, which can be stratified by patients' risk levels based on the cardiometabolic control to guide the differential scheduling of physician encounters in the follow - up. More research is needed to determine how to optimize the frequency of physician encounters for this large and heterogeneous population.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".