Targets for the reduction of antibiotic use in humans in the Transatlantic Taskforce on Antimicrobial Resistance (TATFAR) partner countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unnecessary and inappropriate use of antibiotics in human healthcare is a major driver for the development and spread of antimicrobial resistance; many countries are implementing measures to limit the overuse and misuse of antibiotics e.g. through the establishment of antimicrobial use reduction targets. We performed a review of antimicrobial use reduction goals in human medicine in Transatlantic Taskforce on Antimicrobial Resistance partner countries. On 31 March 2017, the European Centre for Disease Prevention and Control sent a questionnaire to National Focal Points for Antimicrobial Consumption and the National Focal Points for Antimicrobial Resistance in 28 European Union countries, Iceland and Norway. The same questionnaire was sent to the TATFAR implementers in Canada and the United States. Thirty of 32 countries replied. Only nine countries indicated that they have established targets to reduce antimicrobial use in humans. Twenty-one countries replied that no target had been established. However, 17 of these 21 countries indicated that work to establish such targets is currently underway, often in the context of developing a national action plan against antimicrobial resistance. The reported targets varied greatly between countries and can be a useful resource for countries willing to engage in the reduction of antibiotic use in humans.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it