Quality indicators for the prevention of cardiovascular disease in primary care.
Bibliographic record
Abstract
OBJECTIVE: To determine the feasibility and usefulness of collecting 9 previously described quality indicators of cardiovascular disease (CVD) prevention in primary care. DESIGN: Retrospective chart audit. SETTING: Family health team in Hamilton, Ont, comprising approximately 30 000 patients and 25 physicians over 2 sites. PARTICIPANTS: A random sample of community-dwelling men who were 40 to 80 years of age and women who were 50 to 80 years of age on January 1, 2003, and who had complete physical examinations in 2003. MAIN OUTCOME MEASURES: The frequency with which quality indicators were collected during the complete physical examination, whether the collection of these indicators predicted subsequent collection of the same indicators, and physician or patient behavioural changes to reduce the risk of CVD. RESULTS: Of the 237 patient charts reviewed, 142 were of men and 95 were of women. Collection of most of the quality indicators was high (> 50%). Results were adjusted for age, sex, and family health team site. Measurements to check for obesity were collected more frequently in women, while blood pressure measurements and follow-up when required were completed more frequently in men. The relationship between the collection of an indicator and the subsequent times the same indicator was collected was not significant for any of the variables except excess alcohol consumption, in that collection of the excess alcohol consumption indicator led to a significant increase in subsequent collection of that same indicator (P = .0091). Age significantly predicted the number of times cholesterol and blood pressure were repeatedly checked (P = .0074 and P = .0077, respectively). The collection of these indicators was significantly associated with behavioural changes related to CVD prevention on the part of the patient or physician, with collection of the alcohol consumption indicator being the most likely to encourage subsequent behavioural changes. The only indicator to not reach statistical significance for subsequent changes was the cholesterol indicator (P = .08). CONCLUSION: The collection of previously described quality indicators for the primary prevention of CVD in Canada is feasible. Collection of the indicators does not generally predict short-term outcomes; however, collection of most indicators increased the odds of patient or physician behavioural changes for the primary prevention of CVD.
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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; a candidate call from one teacher head, 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".