New concepts in antimicrobial susceptibility testing: the mutant prevention concentration and mutant selection window approach
Bibliographic record
Abstract
Current measurements of antimicrobial susceptibility or resistance utilize a standardized bacterial inoculum (10(5) cfu/mL) exposed to varying drug concentrations in a test tube. Following incubation under ideal conditions, the lowest drug concentration inhibiting growth is the minimum inhibitory concentration (MIC). When the MIC exceeds the amount of drug that can be safely achieved in the body, we call these microorganisms resistant; established breakpoints for various 'bug-drug' combinations are used to categorize microorganisms as susceptible, intermediate or resistant. MIC testing has been used for decades to guide antimicrobial therapy and remains an important measurement for infectious diseases. More recently, the mutant prevention concentration (MPC) has been described as a novel measurement of in vitro susceptibility or resistance and is based on the testing of larger bacterial inocula, i.e. > or =10(9) cfu/mL - such as those associated with some infections in humans and animals. MPC defines the lowest drug concentration required to block the growth of the least susceptible cell present in high density bacterial populations. MPC testing applies to microorganisms considered susceptible to the drug by MIC testing. The mutant selection window (MSW) defines the 'danger zone' for therapeutic drug concentrations. Minimizing the length of time the drug concentration remains in the MSW may reduce the likelihood for resistance selection during therapy. The MSW is bordered by the MIC and MPC values and the drug concentration range between the measured MIC and MPC values defines the MSW. MPC values, when considered with drug pharmacology, may allow prediction on the probability of resistance selection when bacteria are exposed to antimicrobial agents during therapy for infectious diseases. In today's environment, resistance prevention should be a goal of antimicrobial therapy.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".