Partners in Lowering Cholesterol: Comparison of a Multidisciplinary Educational Program, Monetary Incentives, or Usual Care in the Treatment of Dyslipidemia Identified Among Employees
Bibliographic record
Abstract
OBJECTIVE: We sought to assess whether either a low-cost educational intervention or small monetary incentive is more effective than usual care in lowering low-density lipoprotein (LDL) cholesterol among employees. METHODS: Employees with an LDL-C >130 mg/dL were eligible. After receiving on-line educational materials, subjects were assigned to three groups: group 1 received dollar 100 if they reduced their LDL-C by 15% within 6 months, group 2 participated in a multi-disciplinary educational program, and group 3 received no further intervention. RESULTS: In total, 171 employees participated. Baseline mean LDL-C was 156 mg/dL. Approximately 6 months after randomization, mean LDL-C was reduced 17.9 mg/dL (11.3%) in group 1, 17.9 mg/dL (11.5%) in group 2, and 5.5 mg/dL (3.5%) in group 3. Reductions in groups 1 and 2 were statistically superior to group 3 (P = 0.02). CONCLUSIONS: Both an employer directed low-cost educational program and small monetary incentives similarly lowered LDL-C compared with usual care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".