An analysis of the value-added of antibiogram subgroup stratification
Bibliographic record
Abstract
BACKGROUND: Stratified antibiograms are recommended to guide empiric clinical treatment. However, which strata to focus on, the limited number of isolates in identified strata, and the heavy associated workload all pose challenges. This study compares differences in antibiotic susceptibility between a hospital-wide, all-specimens antibiogram and stratified antibiograms in order to identify the value-added of antibiogram stratification. METHOD: Antibiotic susceptibility of bacterial isolates from 2021 at a quaternary-care academic hospital was obtained from published hospital-wide and unit- and specimen-specific stratified antibiograms. Differences in percent susceptibility by organism and drug between the hospital-wide and stratified antibiograms were calculated. Weighted averages of the difference in percent susceptibility were calculated for each stratified antibiogram compared to the hospital-wide antibiogram and unit-wide antibiograms. Differences were shown through heat maps. RESULTS: When compared to a hospital-wide, all-specimens antibiogram, the emergency department (ED) antibiogram showed higher susceptibility, whereas the intensive care unit (ICU) and, particularly, the transplant unit antibiograms exhibited reduced susceptibility. Compared to unit level antibiograms, further stratification within each unit to specimen-specific (syndromic) antibiograms revealed additional differences. In the ED, urine and respiratory-stratified antibiograms had lower susceptibility and blood had higher susceptibility. Compared to unit-specific antibiograms, in the ICU, all specimen-stratified antibiograms had lower susceptibility and in the transplant unit, antibiograms for all specimens but urine had lower susceptibility. CONCLUSION: Using a hospital-wide all-specimens antibiogram may both overcall and under call susceptibility leading to poor empiric antimicrobial choices. Specimen-specific antibiograms stratified by unit best inform empiric therapy for specific populations.
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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 | 0.013 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".