Bibliographic record
Abstract
The results of in vitro antibiotic susceptibility testing can predict the clinical response to treatment and guide the selection of antibiotics. The minimum inhibitory concentration (MIC) of an organism is the lowest concentration of an antibiotic that will inhibit its growth. Bacteria are classified as sensitive, intermediate or resistant based on breakpoint MIC values that are arbitrarily defined and reflect the achievable levels of the antibiotic, the distribution of MICs for the organism and their correlation with clinical outcome. Broth dilution, agar dilution and gradient diffusion (the 'E test'), where twofold serial dilutions of antibiotic are incorporated into tubes of broth, agar plates or on a paper strip, respectively, are different methods to measure the MIC of an organism. The disk diffusion method defines an organism as sensitive or resistant based on the extent of its growth around an antibiotic-containing disk. MIC values are influenced by several laboratory factors. To ensure reproducible results, the laboratory must closely follow methods developed by the National Committee for Clinical Laboratory Standards, which defines standard growth media, incubation temperature and environment, the inoculum and quality control parameters.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".