Surveillance for antimicrobial resistant organisms: potential sources and magnitude of bias
Bibliographic record
Abstract
Surveillance has been recognized as a fundamental component in the control of antimicrobial- resistant infections. Although surveillance data have been widely published and utilized by researchers and decision makers, little attention has been paid to assessment of their validity. We conducted this review in order to identify and explore potential types and magnitude of bias that may influence the validity or interpretation of surveillance data. Six main potential areas were assessed. These included bias related to use of inadequate or inappropriate (1) denominator data, (2) case definitions, and (3) case ascertainment; (4) sampling bias; (5) failure to deal with multiple occurrences, and (6) those related to laboratory practice and procedures. The magnitude of these biases varied considerably for the above areas within different study populations. There are a number of potential biases that should be considered in the methodological design and interpretation of antimicrobial-resistant organism surveillance.
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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 | 0.080 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".