Key considerations on the potential impacts of the COVID-19 pandemic on antimicrobial resistance research and surveillance
Bibliographic record
Abstract
Antibiotic use in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients during the COVID-19 pandemic has exceeded the incidence of bacterial coinfections and secondary infections, suggesting inappropriate and excessive prescribing. Even in settings with established antimicrobial stewardship (AMS) programmes, there were weaknesses exposed regarding appropriate antibiotic use in the context of the pandemic. Moreover, antimicrobial resistance (AMR) surveillance and AMS have been deprioritised with diversion of health system resources to the pandemic response. This experience highlights deficiencies in AMR containment and mitigation strategies that require urgent attention from clinical and scientific communities. These include the need to implement diagnostic stewardship to assess the global incidence of coinfections and secondary infections in COVID-19 patients, including those by multidrug-resistant pathogens, to identify patients most likely to benefit from antibiotic treatment and identify when antibiotics can be safely withheld, de-escalated or discontinued. Long-term global surveillance of clinical and societal antibiotic use and resistance trends is required to prepare for subsequent changes in AMR epidemiology, while ensuring uninterrupted supply chains and preventing drug shortages and stock outs. These interventions present implementation challenges in resource-constrained settings, making a case for implementation research on AMR. Knowledge and support for these practices will come from internationally coordinated, targeted research on AMR, supporting the preparation for future challenges from emerging AMR in the context of the current COVID-19 pandemic or future pandemics.
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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 | 0.102 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.021 | 0.022 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".