Antimicrobial susceptibility testing: An updated primer for clinicians in the era of antimicrobial resistance: Insights from the Society of Infectious Diseases Pharmacists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Antimicrobial susceptibility testing (AST) is a critical function of the clinical microbiology laboratory and is essential for optimizing care of patients with infectious diseases, monitoring antimicrobial resistance (AMR) trends, and informing public health initiatives. Several methods are available for performing AST including broth microdilution, agar dilution, and disk diffusion. Technological advances such as the development of commercial automated susceptibility testing platforms and the advent of rapid diagnostic tests have improved the rapidity, robustness, and clinical application of AST. Numerous accrediting and regulatory agencies are involved in the process of AST and setting and revising breakpoints, including the U.S. Food and Drug Administration and the Clinical and Laboratory Standards Institute. Challenges to optimizing AST include the emergence of new resistance mechanisms, the development of new antimicrobial agents, and generation of new data requiring updates and revisions to established methods and breakpoints. Together, the challenges in AST methods and their interpretation create important opportunities for well-informed clinicians to improve patient outcomes and provide value to antimicrobial stewardship programs, especially in the setting of rapidly changing and increasing AMR. Addressing AST challenges will involve continued development of new technologies along with collaboration between clinicians and the laboratory to facilitate optimal antimicrobial use, combat the increasing burden of AMR, and inform the development of novel antimicrobials. This updated primer serves to reinforce important principles of AST, and to provide guidance on their implementation and optimization.
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.
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 | 0.002 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it