Canadian province-level risk factor analysis of macrolide consumption patterns (2000-2006)
Bibliographic record
Abstract
OBJECTIVES: To assess provincial-level predictors among socioeconomic and influenza rate data for the use of different macrolide antimicrobials in Canada from 2000 to 2006. METHODS: Multivariable models were developed to describe macrolide defined daily doses per capita. RESULTS: Use was highest during October to March for all macrolides. Investigated yearly and provincial patterns differed considerably among the macrolide agents. Associations with socioeconomic variables were similar between clarithromycin and erythromycin, while azithromycin consumption showed some differences in its association with these variables. Consistently, the rate of influenza was significantly associated with increased macrolide use. The influenza rate interacted with socioeconomic variables in some models; as the influenza rate increased, the greatest increase in demand for macrolides occurred in populations with high percentages of low-income individuals, high unemployment levels and low percentages of individuals with bachelor's degrees. CONCLUSIONS: The impact of associations among macrolide consumption, influenza and socioeconomic factors may reflect inappropriate use of these agents to treat viral infections and/or prescribing for secondary infections, and knowledge of the virus versus bacteria problem and accessibility of healthcare. Further research surrounding differences in access to antimicrobial prescriptions and treatment options between advantaged and disadvantaged populations is suggested to further understand the dynamics of antimicrobial use in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".