Increasing the predictive accuracy of the Resistance Gene Identifier by evaluating antimicrobial resistance gene over- and underprediction
Bibliographic record
Abstract
ABSTRACT Computational biology is paving the way towards accessible antimicrobial resistance (AMR) gene (ARG) detection methods to complement canonical gold-standard phenotypic diagnostics for the purposes of antimicrobial surveillance. However, obtaining an accurate depiction of phenotypic resistance through genotypic methods requires addressing potential over- and underprediction of ARGs. This study assessed the manual curation accuracy of bit-score cutoff values associated with bioinformatic models within the Comprehensive Antibiotic Resistance Database (CARD) and the subsequent effects of erroneous cutoff curation, leading to potential Type I and Type II error, on its in silico resistome prediction tool, the Resistance Gene Identifier (RGI). CARD models rarely overpredicted (5 of 3,900 models with >5% false positive rates) but somewhat underpredicted (739 of 3,900 models with >5% false negative rates) resistance-associated sequences and mutations, emphasizing RGI’s conservative prediction algorithms. Isolating curation inaccuracies by AMR gene family, efflux-related families were the main contributors to overprediction (likely due to human curation error), while underprediction was primarily due to beta-lactamase families, the latter finding highlighting systemic curation deficiencies.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".