To Resist or Not to Resist? That is the Dangerous Situation: A Look at Antimicrobial Stewardship in Pediatric Care in North America
Bibliographic record
Abstract
This article will explore the impact of antibiotic resistance on pediatric care in North America, specifically focusing on how it negatively affects patient health outcomes. The mitigation of this issue in everyday health practice will be outlined. Antibiotics are one of the most commonly prescribed medications in children, with a significant number of them being used inappropriately. Considering the growing global threat of antibiotic resistant superbugs, it is vital to develop strategies and programs for decreasing antibiotic misuse and combating antibiotic resistance. Antimicrobial stewardship is one such method of reducing antibiotic resistance and has already shown evidence of improving patient outcomes, such as decreasing risk of future invasive infections, decreasing hospitalization and decreasing mortality. With more awareness to this dangerous issue, we are beginning to see the development and implementation of a variety of practices aimed at using antibiotics judiciously in pediatric patients across North America. This article will address the severity of the issue of antibiotic resistance in pediatric care in North America and highlight how this can be managed using antimicrobial stewardship principles that are practical, evidence-based and easily implementable in healthcare practices. Although there is still much work to be done, small improvements in resistance rates show that we are moving in the right direction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".