Phage‐Antibiotic Combinations for <i>Pseudomonas</i> : Successes in the Clinic and In Vitro Tenuously Connected
Bibliographic record
Abstract
Antimicrobial resistance challenges researchers to innovate strategies to enhance the effectiveness of our existing antibiotics. Bacteriophage (phage, bacterial virus)-antibiotic combinations present a promising synergistic approach, particularly for drug-resistant infections such as those caused by Pseudomonas aeruginosa. This approach offers many advantages: enhanced bacterial killing (both planktonic and biofilm), eliminating persister cells, re-sensitization to drugs, and inhibiting resistance spread by targeting plasmids encoding resistant genes. Interestingly, even phages traditionally excluded from therapy - those capable of entering dormancy in the bacterial host - exhibit unique, potent synergy with antibiotics. Despite these clear in vitro benefits and the comparatively strong performance of phage antibiotic combinations in the clinic, translating in vitro efficacy to patient outcomes remain challenging. The lack of standardized metrics for measuring phage-antibiotic interaction complicates cross-study comparisons. In many instances, it is also difficult to translate these in vitro findings to clinically relevant metrics - for example, increased progeny size in vitro is unlikely to contribute meaningfully to treatment success. Addressing these gaps will allow us to fully harness the potential of phage-antibiotic combinations and bridge the disconnect between in vitro results and clinical success.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".