Comparison of phage host range determination techniques for critical priority pathogens indicates spot testing with dilutions is the most resource consumptive: A meta-analysis
Bibliographic record
Abstract
Antibiotic resistance represents a growing medical crisis and is predicted to cause over 10 million deaths globally per year by 2050. Among the most dangerous resistant bacteria are the critical priority pathogens denoted by the World Health Organization (WHO). Fortunately, phage therapy, a high potential treatment method that uses viruses to infect and lyse bacteria, can successfully clear antibiotic-resistant infections. However, it has not passed clinical trials due to the lack of a streamlined process for phage characterization, highlighting the need to compare phage host range determination (HRD) techniques to simplify this process. To address this, phage primary research papers with HRD data were collected from PubMed and Google Scholar. 56 suitable studies were grouped by critical priority pathogen and employed HRD technique. The quantity (mL) of agar, phage filtrate, and bacterial culture used was recorded. Means for each group were compared using a non-parametric ANOVA. For the E. coli O157:H7 research paper group, plaque testing used significantly less total material and agar than spot testing with dilutions (p = 0.047, p = 0.041), while spot testing without dilutions used significantly less phage filtrate than plaque testing (p = 0.035) and significantly less bacterial culture than spot testing with dilutions (p = 0.013), strongly suggesting that spot testing with dilutions should not be chosen over other methods when conducting HRD as it is the most resource-consumptive. Further investigation into other phage characterization metrics is warranted.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.051 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".