International time trends and differences in topical actinic keratosis therapy utilization
Bibliographic record
Abstract
Background: Actinic Keratoses (AK) are precancerous lesions that can lead to Squamous Cell Carcinoma. International differences in the utilization of topical medications to treat AK are not well described. Objectives: To describe international differences in topical AK medication utilization, including associations of countries' economic status with AK medication utilization. Methods: We used IQVIA MIDAS pharmaceutical sales data for 65 countries (42 high-income, 24 middle-income) from April 2011 to December 2021. We calculated each country's quarterly utilization of medications in grams per 1000 population. We used univariable linear regression to assess the association between country economic status and AK medication utilization. Results: High-income countries used 15.37 more grams per 1000 population of 5-fluorouracil (95% CI: 9.68, 21.05), 4.64 more grams per 1000 population of imiquimod (95% CI: 3.45, 5.83), and 0.32 more grams per 1000 population of ingenol mebutate (95% CI: 0.05, 0.60). Limitations: Missing medication utilization data for some countries. Conclusion: High-income countries use more topical AK therapies than middle-income countries.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".