Optimizing physician‐encounter frequency for type 2 diabetes patients in primary care based on cardiovascular risk assessment: A target trial emulation study
Bibliographic record
Abstract
AIM: To investigate whether the physician-encounter interval for patients with type 2 diabetes (T2D) can be optimized from 2-3 to 4-6 months among those with a calculated 10-year cardiovascular disease (CVD) risk score of less than 20% without compromising their long-term outcomes. MATERIALS AND METHODS: Using territory-wide public electronic medical records in Hong Kong, we emulated a target trial to compare the effectiveness of the physician-encounter intervals of 4-6 versus 2-3 months for T2D patients without prior CVDs and with a predicted risk for CVDs of less than 20% (i.e. those patients not in the high-risk category). Propensity score matching was used to emulate the randomization of participants at baseline, where 42 154 matched individuals were included for analysis. The marginal structural model was applied to estimate the hazard ratio (HR) for CVD incidence and all-cause mortality, the incidence rate ratio of secondary and tertiary care utilization, as well as the between-group differences in HbA1c, blood pressure and cholesterol levels. RESULTS: During a follow-up period of up to 12 (average: 5.1) years, there was no significantly increased risk of CVD in patients with physician-encounter intervals of 4-6 months compared with those patients with physician-encounter intervals of 2-3 months (HR [95% confidence interval {CI}]: 1.01 [0.90, 1.14]; standardized 10-year risk difference [95% CI]: -0.1% [-0.7%, 0.6%]), nor for all-cause mortality (HR: 1.00 [0.84, 1.20]; standardized 10-year risk difference: -0.1% [-0.5%, 0.3%]). Additionally, there was no observable difference in the utilization of secondary and tertiary care or key clinical parameters between these two follow-up frequencies. CONCLUSIONS: For T2D patients with a calculated 10-year CVD risk of less than 20%, the interval of regular physician encounters can be optimized from 2-3 to 4-6 months without compromising patients' long-term outcomes and saving substantial service resources in primary care.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".