Value of an Aggregate Index in Describing The Impact Of Trends in Antimicrobial Resistance for<i>Escherichia coli</i>
Bibliographic record
Abstract
BACKGROUND: Drug resistance indexes (DRIs) quantify the cumulative impact of antimicrobial resistance on the likelihood that a given pathogen will be susceptible to antimicrobial therapy. OBJECTIVE: To derive a DRI for community urinary tract infections caused by Escherichia coli in British Columbia for the years 2007 to 2010, and to examine trends over time and across patient characteristics. METHODS: Indication-specific utilization data were obtained from BC PharmaNet for outpatient antimicrobial prescriptions linked to diagnostic information from physician payment files. Resistance data for E coli urinary isolates were obtained from BC Biomedical Laboratories (now part of LifeLabs Medical Laboratory Services). DRIs were derived by multiplying the rate of resistance to a specific antimicrobial by the proportional rate of utilization for that drug class and aggregating across drug classes. Higher index values indicate more resistance. RESULTS: Adaptive-use DRIs remained stable over time at approximately 18% (95% CI 17% to 18%) among adults ≥15 years of age and approximately 28% (95% CI 26% to 31%) among children <15 years of age. Similar results were observed when proportional drug use was restricted to the baseline year (ie, a static-use model). Trends according to age group suggest a U-shaped distribution, with the highest DRIs occurring among children <10 years of age and adults ≥65 years of age. Males had consistently higher DRIs than females for all age groups. CONCLUSIONS: The stable trend in adaptive-use DRIs over time suggests that clinicians are adapting their prescribing practices for urinary tract infections to local resistance patterns. Results according to age group reveal a higher probability of resistance to initial therapy among young children and elderly individuals.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".