Bibliographic record
Abstract
To theEditor—We thank Chironda et al for their comments regarding our study [1]. We agree that our intervention reduced antimicrobial therapy for asymptomatic bacteriuria (ASB) without attempting to reduce the number of submitted urine cultures. We applaud their implementation of frontline ownership in the emergency department to improve urine culture ordering practices and the 24% reduction achieved [1]. Although we support efforts to directly address the complex behaviors that lead to unnecessary urine culture ordering, we specifically bypassed such efforts in our study because they have in the past been difficult or impossible to sustain [2]. We propose that the redesign of urine culture processing systems may offer more sustainable improvement in urine culture ordering practices, in addition to decreasing antimicrobial therapy for ASB. Our proof-of-concept study was designed to verify the hypothesis that the majority of antimicrobial therapy for ASB occurs in response to positive results from urine cultures submitted with a low pretest probability of urinary tract infection (UTI) [3]. The corresponding change concept that we evaluated was to render these results available only upon request; we continued to process urine cultures so that results would be available promptly if needed. The few telephone requests received and the rapid change in antimicrobial prescribing observed supported our hypothesis. Although time was required to process specimens, no investment of time was required of any frontline staff or educators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.131 | 0.057 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".