Antibiotic tolerance among clinical isolates: mechanisms, detection, prevalence, and significance
Bibliographic record
Abstract
SUMMARY Antibiotic treatment failures in the absence of resistance are not uncommon. Recently, attention has grown around the phenomenon of antibiotic tolerance, an underappreciated contributor to recalcitrant infections first detected in the 1970s. Tolerance describes the ability of a bacterial population to survive transient exposure to an otherwise lethal concentration of antibiotic without exhibiting resistance. With advances in genomics, we are gaining a better understanding of the molecular mechanisms behind tolerance, and several studies have sought to examine the clinical prevalence of tolerance. Attempts have also been made to assess the clinical significance of tolerance through in vivo infection models and prospective/retrospective clinical studies. Here, we review the data available on the molecular mechanisms, detection, prevalence, and clinical significance of genotypic tolerance that span ~50 years. We discuss the need for standardized methodology and interpretation criteria for tolerance detection and the impact that methodological inconsistencies have on our ability to accurately assess the scale of the problem. In terms of the clinical significance of tolerance, studies suggest that tolerance contributes to worse outcomes for patients (e.g., higher mortality, prolonged hospitalization), but historical data from animal models are varied. Furthermore, we lack the necessary information to effectively treat tolerant infections. Overall, while the tolerance field is gaining much-needed traction, the underlying clinical significance of tolerance that underpins all tolerance research is still far from clear and requires attention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".