How to Measure Antibiotic Resistance Using Empiric Therapy Indices
Bibliographic record
Abstract
Background. Concise, standard, simple measures of the diversity of antibiotic resistance and its impact on health are needed to effectively communicate the burden of resistance to a wide audience, understand trends, evaluate interventions, and motivate investment. Methods. We developed two complementary indices of antibiotic resistance. The empiric resistance index (ERI) measures the coverage provided by available drugs for empiric therapy. The empiric options index (EOI) measures the value of multiple drugs, on the understanding that drug use will lead to resistance so more options are better. The indices account for the availability of treatment options and the relative importance of pathogens. Results. Scenarios show the behaviour and usefulness of the indices. In the ICUs of a large Toronto hospital the ERI remains high (98%) because a few drugs provide good coverage, but 50 to 60% of treatment potential measured by the EOI has been lost. Ceftazidime-avibactam and ceftolozane-tazobactam could increase the EOI by providing empiric coverage of Gram-negative infections. Carbapenemase (KPC)-producing Enterobacteriacea threaten empiric therapy (ERI = 57%–74%, EOI = 1.9–2.8). Pandrug-resistant Acinetobacter poses less threat (ERI = 95%, EOI = 4.8–6) because it causes less disease. Increasing MRSA prevalence would have little impact (ERI = 98%, EOI = 4.8–5.6) because many Gram-positives are already resistant to β-lactams. Aminoglycoside resistance threatens the EOI (ERI = 97%, EOI = 3.7–4.9) because aminoglycosides cover Gram-negative infections. Conclusion. The ERI and EOI measure available empiric coverage and the value of multiple treatment options, providing a meaningful summary of resistance that can be calculated from cumulative antibiogram data. These indices can be used to understand trends, assess threats, and assess interventions. Disclosures. All authors: No reported disclosures.
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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 | 0.012 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".