Cross-national comparison of levothyroxine utilization in four developed countries
Bibliographic record
Abstract
Context: While hypothyroidism is prevalent globally, there may be considerable variation in the use of therapies to treat this condition. Aims: The aim was to examine temporal trends of levothyroxine utilization in Canada, Greece, Ireland and the United States. Settings and Design: A cross-sectional, population-based time series study was conducted to assess monthly utilization rates from January 1, 2005 to September 20, 2011, in Canada, Greece, Ireland and the United States. The primary outcome measure was the monthly rate of levothyroxine utilization for each country analyzed. Materials and Methods: Levothyroxine utilization data were obtained from IMS Health Inc., and temporal trends in monthly units dispensed per 1,000 population were examined. Statistical Analysis Used: Time series analysis was used to examine temporal trends in levothyroxine utilization. Results: While levothyroxine utilization rates increased over time in all regions, considerable differences were noted between regions - 80% relative difference in average monthly utilization between the highest (Greece - 1,664 units/1,000 population) and lowest (Ireland - 925 units/1,000 population) utilization countries was observed. We observed a nearly 3.5-fold difference in utilization of moderate-to-high strength doses (100 μg+) of levothyroxine between the countries examined. Conclusions: We noted considerable regional variation in the use of levothyroxine. Further research is needed to understand the drivers of these variations in utilization rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".