Relevance of the Consensus Principles for Appropriate Antibiotic Prescribing in 2022
Bibliographic record
Abstract
BACKGROUND: In the late 1990s, as a response to rising antimicrobial resistance (AMR), an independent multinational, interdisciplinary group was formed specifically targeting primary care antibiotic prescribing for community-acquired respiratory tract infections (CA-RTIs). The group comprised senior clinicians from Canada, Israel, Spain, Sweden, UK and USA. The group's objectives were to provide recommendations for antibiotic stewardship in the community because, whilst it was widely accepted that inappropriate antibiotic use was contributing to AMR, it remained difficult to change prescribing behaviour. The group aimed to identify principles underlying appropriate antibiotic prescribing and guideline formulation to reduce morbidity from CA-RTIs, limit therapeutic failure and, importantly, curb AMR emergence. The group published a report in 2002, which has become known as the Consensus Principles. OBJECTIVES: (i) To consider the relevance of the Consensus Principles in 2022 by reviewing current global approaches to rising AMR. A wide range of factors, such as antibiotic overuse, most recently seen in COVID-19 patients, are still driving rising AMR even though there has been a high-level international response to the AMR threat; and (ii) as an introduction to this Supplement, which reports the findings of analyses of how AMR is being addressed in nine disparate countries (Brazil, India, Kuwait, Mexico, Pakistan, Russia, Saudi Arabia, Türkiye and Vietnam). Understanding how these initiatives are being pursued in different countries helps identify areas where more information is needed. CONCLUSIONS: Adherence to the Consensus Principles remains as important now as it was in 2002. Achieving appropriate antibiotic prescribing is a vital objective in order that the right patient receives the right antibiotics at the right time to ensure optimal clinical outcomes while at the same time helping to limit further increases in AMR.
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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 | 0.110 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".