Are patients suffering from stable angina receiving optimal medical treatment?
Bibliographic record
Abstract
There is good evidence for the use of antiplatelet, beta-blocker and lipid-lowering drugs in the treatment of ischaemic heart disease, but few data on how these medications are used in treating stable angina pectoris. We examined prescription profiles for a sample of patients aged > or =65 years with stable angina, to compare the profiles to local guidelines and to explore the determinants of these profiles, in a cross-sectional study. We identified 11 141 individuals from the Quebec provincial out-patient pharmaceutical database for the period 1 June 1996 to 31 May 1997, and examined the percentage of these patients with and without associated co-morbidities receiving antiplatelet, beta-blocker and lipid-lowering medications. We used hierarchical modelling to examine the role of patient and physician characteristics in explaining the variation in the use of these medications. Calcium-channel blockers were the class of anti-ischaemic drugs most prescribed (63%). Beta-blockers were prescribed in 52.1% of patients. Antiplatelet and lipid-lowering drugs were prescribed to 56.8% and 32.6%, respectively. Increasing age and female gender made patients less likely to be prescribed these treatments. General practitioners were less likely than cardiologists to prescribe beta-blockers and lipid-lowering drugs (OR 0.79, CI 95% 0.68-0.91 and OR 0.77, CI 95% 0.66-0.91, respectively). There is a general under-use of antiplatelet, beta-blocker and lipid-lowering medications in the treatment of stable angina pectoris patients, possibly leading to adverse patient outcomes.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".