Long-term forecasting and evaluation of medicine consumption for the ATC class H with a focus on thyroid hormones in OECD countries using ARIMA models
Bibliographic record
Abstract
Thyroid hormones are among the most prescribed medicines. In many countries, there are shortages combined with evidence of overuse and irrational prescribing. An analysis was conducted for ATC class H with a focus on thyroid hormones for OECD countries. This study aims to evaluate prescribing behaviours, forecast long-term developments and promote rational prescribing behaviour. The ARIMA(2,1,2) (autoregressive integrated moving average) model successfully predicted the future for 30 OECD countries and the non-OECD country Croatia until 2040. An upward trend is forecast for 18 countries, including Austria (+ 5.7%), Chile (+ 220.0%), Czechia (+ 52.8%), Denmark (+ 15.6%), Estonia (+ 87.8%), Greece (+ 238.7%), Hungary (+ 5.7%), Iceland (+ 18.6%), Italy (+ 42.9%), Latvia (+ 83.7%), Lithuania (+ 131.2%), Portugal (+ 106.7%), Slovakia (+ 182.1%), Slovenia (+ 57.4%), Spain (+ 162.8%), Turkey (+ 168.7%), the United Kingdom (+ 138.1%) and Croatia (+ 190.6%). A downward trend is forecast for 13 countries, including Australia (-3.4%), Belgium (-38.8%), Canada (-95.1%), Costa Rica (-79.5%), Finland (-14.7%), France (-100.0%), Germany (-16.4%), Israel (-21.6%), Korea (-100.0%), Luxembourg (-100.0%), the Netherlands (-35.9%), Norway (-23.6%) and Sweden (-43.6%). The reliability and accuracy of the forecasts varies, being influenced by data quality. While a downward trend is favoured, an upward trend is seen as problematic. Increasing trends predominate in Southern and Eastern Europe and Latin America, while decreasing trends predominate in Northern and Western Europe and the Asia-Pacific Region. Some external factors affect all countries, like an increasing prevalence of thyroid disease. There is evidence of cultural influences on prescribing behaviour. While there is evidence of inappropriate use in countries where prescriptions are predicted to increase, measures to restrict the use of thyroid hormones are more common in countries with a recently reported and predicted declining trend.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".