Targets for the reduction of antibiotic use in humans in the Transatlantic Taskforce on Antimicrobial Resistance (TATFAR) partner countries
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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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 | 0.030 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".