Phage Therapy: Navigating the Mechanisms, Benefits, and Challenges in the Fight Against Multidrug-Resistant Infections
Bibliographic record
Abstract
ABSTRACT Background The emergence of multidrug-resistant (MDR) infections represents one of the most critical challenges faced by contemporary medical practice. The World Health Organization (WHO) has identified antibiotic resistance as a significant threat to global health, with MDR bacteria leading to increased morbidity, mortality, and healthcare costs [1] . Objective (PICOS) This review examines (P) MDR infection patients receiving (I) short-term (0-14 day) phage therapy versus (C) long-term/antibiotic treatments, assessing (O) efficacy (eradication rates) and safety (adverse events) through (S) clinical trial/case study analysis. Primary & Secondary Outcome Measures Efficacy (bacterial eradication) and safety (adverse events) of 0–14 day phage therapy, compared to conventional antibiotics or longer phage regimens. Intervention Application of bacteriophages (viruses that infect and lyse bacteria) as targeted antimicrobial agents. Methods A comprehensive review of clinical trials, case studies, and experimental models from peer-reviewed literature. Results Phage therapy demonstrates high bacterial specificity, adaptability to resistance, and synergistic effects with antibiotics. Article Summary While phage therapy offers a promising alternative to antibiotics, its clinical integration faces regulatory, logistical, and safety challenges. Strengths and limitations of this study The strengths include detailed analysis of phage mechanisms, clinical applications, and therapeutic potential. The limitations are limited large-scale randomized controlled trials (RCTs) and standardized treatment protocols & last is This review was not registered in PROSPERO. Conclusion Phage therapy holds transformative potential in combating MDR infections but requires further research, regulatory standardization, and clinical validation.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".