Antimicrobial Stewardship in Daily Practice: Managing an Important Resource
Bibliographic record
Abstract
Antimicrobial stewardship is a recent concept that embodies the practical, judicious use of antimicrobials to decrease adverse outcomes from antimicrobials while optimizing the treatment of bacterial infections to reduce the emergence of resistant pathogens. The objectives of the present statement are to illustrate the principles of antimicrobial stewardship and to offer practical examples of how to make antimicrobial stewardship part of everyday hospital and outpatient practice. Vital components of antimicrobial stewardship include appropriate testing to diagnose whether infections are viral or bacterial, and using clinical follow-up rather than antibiotics in cases in which the child is not very ill and uncertainty exists. Other specific, important actions include questioning whether positive urine cultures are contaminated when there is no evidence of pyuria or inflammatory changes, and obtaining a chest radiograph to support a diagnosis of bacterial pneumonia. Optimizing the choice and dosage of antimicrobials also reduces the probability of clinical failures and subsequent courses of antimicrobials. A list of common clinical scenarios to promote stewardship is included.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".