160A Simulation Study to Assess Indicators of Antimicrobial Use as Predictors of Resistance
Bibliographic record
Abstract
Background. Indicators of antimicrobial (AM) use have been described previously, but the optimal indicator for predicting AM resistance in hospital settings, especially when including pediatric populations, is unknown. This simulation study aimed to assess if a significant difference could be found between these indicators' accuracy as predictors of AM resistance, even with entire networks of intensive care units (ICUs). Methods. Ten different resistance / AM use combinations (combinations) were studied. Simulations were run to find out if Québec's network of ICUs or the National Healthcare Safety Network (NHSN) ICUs could have allowed the detection of predetermined differences between the most accurate and 1) the second most accurate indicator, and 2) the least accurate indicator, in more than 80% of simulations. For each indicator, simulated absolute errors were generated, for each ICU and each 4-week period, over 4 years of surveillance (absolute error = |observed prevalence or incidence – predicted prevalence or incidence|. Absolute errors in prediction were generated following a binomial distribution, using mean absolute errors (MAEs) observed in data from 9 ICUs as the average proportion; simulated MAEs were then compared using t-tests. This was repeated 1,000 times for each scenario. Results. Main results are presented in the table. Number of resistance / AM use combinations for which a power of 80% was reached, for different scenarios. Conclusion. The two most accurate indicators of AM use would often offer similar predictions of resistance, even in large networks. The least accurate indicators could frequently be distinguished, but not always, especially in the Québec network, which is smaller. 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".