Review: intensified patient care may improve adherence to lipid-lowering medication in primary or secondary prevention of CV diseaseCommentary
Bibliographic record
Abstract
A Schedlbauer Dr A Schedlbauer, University of Nottingham, Nottingham, UK; angela.schedlbauer@nottingham.ac.uk Are interventions for improving adherence to lipid-lowering medications (LLMs) effective for primary or secondary prevention of cardiovascular (CV) disease in ambulatory settings? ### Data sources: Medline, CINAHL, EMBASE/Excerpta Medica, Cochrane Central Register of Controlled Trials, and PsycINFO (searched in Nov 2005); reference lists; authors; and experts. ### Study selection and assessment: randomised controlled trials (RCTs) in any language that compared interventions to increase adherence to LLMs (eg, nicotinic acid or niacin, anion-exchange resins, statins) with no intervention or usual care in adults for primary or secondary prevention of CV disease in ambulatory care settings. Quality assessment of individual studies was based on avoidance of selection, performance, attrition, and detection biases according to criteria from the Cochrane Reviewers’ Handbook; studies were categorised …
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 | 0.007 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".