Bibliographic record
Abstract
BACKGROUND: Prescription drug expenditures in North America have nearly doubled in the past 5 years, creating intense pressure for all public and private benefits managers and policymakers. OBJECTIVE: The objective of this study was to describe age-specific drug expenditure trends from 1996 to 2002 for the Canadian province of British Columbia. STUDY DESIGN: This study shows changes in expenditures per capita quantified for 5 age categories: residents aged 0 to 19, 20 to 44, 45 to 64, 65 to 84, and 85 and older. The cost impacts of 7 determinants of prescription drug expenditures are quantified. DATA: This study describes population-based, patient-specific pharmaceutical data showing the type, quantity, and cost of every prescription drug purchased by virtually all residents of British Columbia. RESULTS: Population-wide expenditures per capita grew at a rate of 11.6% per annum. Growth was primarily driven by the selection of more costly drugs per course of treatment and increases in the number concomitant treatments received per patient. Population aging did not have a major impact on expenditures. However, expenditure per capita grew most rapid among residents aged 45 to 64, the cohort that expended most over the period. The aging of this demographic cohort may threaten the financial viability of age-based drug benefit programs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".