ResistanceOpen: A web application for global antibiotic resistance monitoring
Bibliographic record
Abstract
Background: Antibiotic resistance is a major public health concern. Despite this, we have a poor understanding of the burden and regional patterns of antibiotic resistance using existing techniques. Non-traditional approaches may be able to complement and support current programs for antibiotic resistance surveillance. We sought to use existing but disparate online antibiotic resistance data to monitor regional patterns of antibiotic resistance. Methods: We developed a web-based and mobile compatible platform for identifying and analyzing regional patterns of antibiotic resistance using existing resistance indices. Antibiotic resistance indices were reviewed and abstracted by experienced data curators. Antibiotic resistance information was captured for up to 35 pre-specified bacteria and 27 pre-specified antibiotics. Additional variables identified included (1) year of index isolates, (2) laboratory standards employed, (3) specimen site, (4) hospital site, and (5) hospital/laboratory/surveillance body classification. Aggregated antibiotic resistance data for the United States and Canada were compared to existing national and state surveillance estimates for validation purposes. Measures of variability of antibiotic susceptibility were evaluated for the United States and Canada to determine magnitudes of differences within countries. Results: Over 850 indices of resistance globally were identified and abstracted, with over 5 million total isolates, and from 340 separate locations. Indices spanned 41 countries, 6 continents, 43/50 US states, and 8/10 Canadian provinces. Aggregated resistance values for the United States and Canada for the years 2013 and 2014 showed agreement with reported values ranging from 94-97%. State-specific resistance estimates, for the United States, showed an agreement of 92%. Large differences in antibiotic resistance were seen within countries. Conclusion: Utilizing non-traditional approaches, we created a web-based platform for aggregating antibiotic resistance indices to support antibiotic resistance surveillance globally. Estimates derived using these techniques generate estimates comparable to traditional surveillance data and may prove useful in under-resourced regions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.036 |
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".