A Platform for Monitoring Regional Antimicrobial Resistance, Using Online Data Sources: ResistanceOpen
Bibliographic record
Abstract
Background: Our understanding of the global burden of antimicrobial resistance is limited. Complementary approaches to antimicrobial resistance surveillance are needed. Methods: We developed a Web-based/mobile platform for aggregating, analyzing, and disseminating regional antimicrobial resistance information. Antimicrobial resistance indices from existing but disparate online sources were identified and abstracted. To validate antimicrobial resistance data, in the absence of regional comparators, US and Canadian indices were aggregated and compared to existing national and state estimates. Measures of variability of antimicrobial susceptibility were determined for the United States and Canada to evaluate magnitudes of differences within countries. Results: Over 850 resistance indices globally were identified and abstracted, totaling >5 million isolates, from 340 unique locations. Resistance index coverage spanned 41 countries, 6 continents, 43 of 50 US states, and 8 of 10 Canadian provinces. When compared to reported values, aggregated resistance values for the United States and Canada during 2013 and 2014 demonstrated agreements ranging from 94% to 97%. For the United States, state-specific resistance estimates demonstrated an agreement of 92%. Large differences in antimicrobial susceptibility were seen within countries. Conclusions: Using existing nontraditional data sources, we have developed a Web-based platform for aggregating antimicrobial resistance indices to support monitoring of regional antimicrobial resistance patterns.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".