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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".