P1877LDL-C treatment patterns and associated outcomes in patients with type 2 diabetes and CVD: insights from TECOS
Bibliographic record
Abstract
Background: Patients with diabetes mellitus (DM) are at increased risk for cardiovascular (CV) events. Current CV Guidelines recommend LDL-C levels ≤70 mg/dL (1.8 mM) for DM patients with CV disease while a recent endocrinology guideline (AACE) proposes an LDL-C target of ≤55 mg/dL for DM and a recent acute coronary syndrome. Purpose: Using data from TECOS, an international CV outcomes trial of sitagliptin vs placebo, we sought to 1) determine contemporary LDL-C treatment among patients with DM and CV disease; and 2) determine the associations between baseline LDL-C and subsequent risk for 5-year CV outcomes. Methods: Association between baseline LDL-C and 5 year MACE (CV death, non-fatal MI, or non-fatal stroke) was assessed using multivariable adjusted Cox regression analysis. Results: Overall, 11,066/14,671 (75.4%) TECOS patients had a baseline LDL-C measurement. Their median (25th, 75th percentiles) age was 65 years (60, 71), 71.5% were male, the median duration of DM was 10 years (6, 16), HbA1c 7.2% (6.8, 7.6). At baseline, 82.5% and 5.8% of patients were taking statins and ezetimibe respectively. LDL-C was ≤55 mg/dL in 14.3%; 55.1 to 70 in 18.4%, 70.1 to 100 in 35% and >100 in 32.3%. Each 10 mg/dL of higher LDL-C was associated with increased risk of CV death (HR 1.06; 95% CI 1.04–1.09) and MACE (HR 1.05; 95% CI 1.03–1.07). The probability of MACE as a function of baseline LDL-C, along with 95% confidence limits, is depicted in the Figure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".