A One Health perspective on the use of genotypic methods for antimicrobial resistance prediction
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 resistance is a global One Health concern with critical implications for the health of humans, animals, and the environment. Phenotypic methods of bacterial culture and antimicrobial susceptibility testing remain the gold standards for the detection of antimicrobial resistance and appropriate patient care; however, genotypic-based methods, such as PCR, whole genome sequencing, and metagenomic sequencing, for detection of genes conferring antimicrobial resistance are increasingly available without inclusion of appropriate standards for quality or interpretation. Misleading test results may lead to inappropriate antimicrobial treatment and, in turn, poor patient outcomes and the potential for increased incidence of antimicrobial resistance. This article explores the current landscape of clinical and methodological aspects of antimicrobial susceptibility testing and genotypic antimicrobial resistance test methods. Additionally, it describes the limitations associated with employing genotypic-based test methods in the management of veterinary patients from a One Health perspective. The companion Currents in One Health by Maddock et al, AJVR, March 2024, addresses current and future needs for veterinary antimicrobial resistance research.
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 | 0.004 |
| 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 it