Current Antibiotic Susceptibility Test Underestimates Minority Resistance: Implications for High-Risk Infections
Bibliographic record
Abstract
Antibiotic susceptibility testing (AST) reports classify isolates as “susceptible” despite potential undetected resistant subpopulations—a phenomenon termed susceptibility heterogeneity (SH). Found in 15–97% of clinical isolates of Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Klebsiella pneumoniae, SH arises from heteroresistance or polyclonal diversity and may evade standard low-inoculum protocols. Clinically, this can lead to treatment failure, particularly in high-risk cases including immunocompromised patients, bloodstream infections, transplant recipients, and situations where minor resistant subpopulations significantly affect outcome. We argue that ethical principles of non-maleficence, transparency, and equity now compel laboratories to acknowledge this limitation. A simple annotation—“Limited susceptibility possible; resistant subpopulations may not be detected”—should accompany “susceptible” results in immunocompromised patients. High-risk cases warrant enhanced testing. This commentary calls for zone inspection, staff training, and Clinical and Laboratory Standards Institute (CLSI)/European Committee on Antimicrobial Susceptibility Testing (EUCAST) guideline updates to reflect SH. Transparency enhances clinical decision-making without implying diagnostic fault.
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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 | 0.075 | 0.382 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".