Cardiovascular risk thresholds for intensifying primary care encounter frequency for patients with type 2 diabetes mellitus: a target trial emulation
Bibliographic record
Abstract
BACKGROUND: Determining optimal timing for intensifying the frequency of physician encounters for type 2 diabetes mellitus (T2DM) requires trade-offs between timely care and clinician burden. We aimed to investigate age-specific cardiovascular disease (CVD) risk thresholds used for intensifying encounter frequency in patients with T2DM in primary care. METHODS: Using population-based public electronic health records from the Hospital Authority Clinical Management System in Hong Kong, we used data from patients with a baseline 10-year CVD risk of lower than 20% and a regular follow-up interval of 4-6 months at public primary care clinics. We compared different CVD risk thresholds (> 20% v. 30%) at which to shorten follow-up intervals to 3 months or less. We investigated age-specific effects by categorizing patients into 4 age groups (< 50, 50-59, 60-69, and ≥ 70 yr). In the causal framework of the target trial emulation, we used a dynamic marginal structural model to estimate absolute risk differences for 5-year incidence of CVD, under the assumption of no unmeasured confounding. RESULTS: We identified 44 813 patients. Compared with the risk threshold of 20%, adopting the threshold of 30% did not increase risk of overall CVD in patients younger than 50 years (absolute risk difference 0.2%, 95% confidence interval [CI] -0.6% to 1.0%]) and aged 50-59 years (absolute risk difference 0.7%, 95% CI -0.1% to 1.4%). However, we observed an increased risk in older patients, aged 60-69 years (absolute risk difference 1.5%, 95% CI 0.6% to 2.5%) and 70 years or older (absolute risk difference 2.7%, 95% CI 0.6% to 4.9%). INTERPRETATION: When adopting a less stringent CVD risk threshold of 30% to intensify encounter frequency, we found progressively increasing risks with age for 5-year CVD incidence, and age 60 years appears to be a point where the risk becomes pronounced. Our findings suggest that a less stringent threshold could be considered for patients with T2DM who are younger than 60 years but not for those older than 60 years.
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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 | 0.049 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".