Physician characteristics in relation to cardiovascular drugs commonly prescribed for hypertension in Nova Scotia.
Bibliographic record
Abstract
BACKGROUND: Cardiovascular drugs are the most frequently prescribed class of drugs in Canada. Among them, drugs used to treat hypertension are the single largest component. Variability in how these drugs are prescribed should be based on the specific characteristics of patients. However, some evidence shows that physician characteristics can also play a substantial role in prescribing trends. Such variation is also associated with varying beneficial and adverse patient outcomes. PURPOSE: To determine whether prescribing patterns of drugs used to treat hypertension in elderly patients in Nova Scotia varied by physician characteristics. METHODS: A retrospective, population-based descriptive study was done using the Nova Scotia Pharmacare program data for the fiscal year 1995/96. The unit of analysis was the individual physician. All drugs indicated in the management of hypertension were included for the analysis. RESULTS: Of 1466 physicians included in the analysis, 1004 were family physicians (FPs) and/or general practitioners (GPs), 155 were internal medicine specialists and 307 were other specialists. Fifty-eight per cent of 103,193 eligible senior citizens received at least one of the study medications. FPs and/or GPs prescribed 95.9% of all the study drugs. Internists prescribed proportionately fewer angiotensin-converting enzyme inhibitors, thiazides and other diuretics compared with the FPs and/or GPs but more beta-blockers and calcium channel blockers. A large proportion of the FPs and/or GPs (55.3%) prescribed less than 10% of the total day's supply of drugs, whereas a small proportion of FPs and/or GPs (16.3%) prescribed 52.6% of all the study drugs. There was no variation in the distribution of the types of antihypertensives prescribed based on physician age, sex or volume of prescribing. A slight variation in prescribing was seen with location of practice. CONCLUSIONS: Patterns of prescribing cardiovascular drugs used to treat hypertension were remarkably unaffected by physician characteristics. This finding counters other evidence in the literature that has raised concerns over prescribing patterns of certain types of physicians. Prescribing patterns may vary for other drug classes, but for this group of antihypertensives, little variability was found.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".