Rising Antimicrobial Resistance: Not Only Attention Situation Demand Immediate Action
Bibliographic record
Abstract
Sir, Each year, thousands of people succumb to resistant bacteria, and many researchers fear the day may be coming when diseases that were once treatable will become fatal.The causes of antimicrobial resistance (AMR) are complex and multifaceted, including inappropriate prescriptions, overuse, and/or misuse of broad-spectrum antibiotics.1 However, one of the major causes of AMR in the population is the overuse of antibiotics without proper indications.AMR is a global issue that does not respect borders.2,5 It is present in almost every country.About 2.8 million people encounter an antibiotic-resistant infection, and more than 18,000 hospitalised patients acquire infections that are resistant to antimicrobial drugs every year.Almost 90% of antibiotics are prescribed by primary health care physicians.1 The broad range of disparities in prescribing antibiotics is principally due to the lack of clarity about AMR among healthcare professionals.It is critical to raise AMR awareness among healthcare professionals to ensure that antibiotics are prescribed, distributed, and administered appropriately.It is apparent from the literature that interventions, grounded in the approaches to restrict or regulate access to resources, can help to manage issues adequately.3 The importance and need for "institutional empiric treatment guidelines" with ongoing training for healthcare professionals to improve prescribing traditions can not be overemphasized.Presently, there are no concrete institutional or national guidelines implemented across the country's health facilities that can support optimal prescribing practices and reduce the misuse of antimicrobials............
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".