Adequacy of reporting monitoring regimens of risk factors for cardiovascular disease in clinical guidelines: systematic review
Bibliographic record
Abstract
OBJECTIVE: To assess the reporting of monitoring recommendations in guidelines on the prevention and treatment of cardiovascular disease. DATA SOURCES: Medline, Trip database, National Guideline Clearinghouse, and databases containing guidelines published from January 2002 to February 2010. Data selection Three major risk factors for cardiovascular disease: cholesterol level, smoking, and hypertension. The primary outcome was the frequency with which the guidelines dealt with monitoring of risk factors. Secondary outcomes were completeness of monitoring recommendations, defined by the presence of what to monitor, when to monitor, what to do if the targets or variables were not met, and the reported level or strength of the evidence. RESULTS: 117 guidelines were identified, 84 (72%) of which contained a section on lipids. Of those guidelines with a section on lipids, 53% (n = 44) provided no information or specific recommendations on what to monitor, 51% (n = 43) provided no information on when to monitor, and 64% (n = 54) provided no guidance on what to do if the target was out of range. Guidelines for hypertension (n = 79) and smoking (n = 65) were little better, with 63% (n = 50) and 54% (n = 35), respectively, providing no recommendation for what to monitor. The number of guidelines that explicitly referenced the level of evidence for monitoring was low, with most of the recommendations based on weak levels of evidence. CONCLUSION: Many guidelines for cardiovascular disease do not report clearly what to monitor and what to do if a change is detected. If no evidence is available to support a specific monitoring schedule, this should be explicit in the guideline, with a description of the new research that would fill the gap.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.521 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".