The Impact of Empirical Management of Acute Cystitis on Unnecessary Antibiotic Use
Bibliographic record
Abstract
BACKGROUND: Guidelines for the management of acute cystitis support empirical antibiotic treatment; however, up to half of symptomatic women have negative urine cultures. OBJECTIVE: To determine whether empirical treatment leads to unnecessary antibiotic prescriptions in women with symptoms of acute cystitis. METHODS: A cohort of 231 women (defined as females aged 16 years and older) presenting to family physicians' offices with symptoms of cystitis underwent a standardized clinical assessment, urine dip testing, and culture. Recommendations for urine testing and antibiotic treatment under 3 empirical strategies were compared with observed physician management and a logistic regression model for the outcomes of antibiotic prescriptions, urine culture testing, and unnecessary antibiotics, defined as a prescription where the subsequent urine culture was negative. RESULTS: There were 123 positive urine cultures (53.3%). Physicians prescribed antibiotics to 186 women (80.9%), of whom 74 (39.8%) were culture negative. Unnecessary antibiotic use was similar for 2 guidelines recommending empirical antibiotic treatment without testing for pyuria (41.4% and 40.6%). Treating women with classic cystitis symptoms and pyuria would have decreased unnecessary antibiotic use (26.2%; P =.02) but resulted in fewer women with confirmed urinary tract infection receiving immediate antibiotics (66.4% vs 91.8% usual care; P<.001). A derived prediction model incorporating testing for pyuria and nitrites would also have reduced unnecessary antibiotic use (27.5%; P =.03), but more women with confirmed urinary tract infection would have received immediate antibiotics (81.3%; P =.01). CONCLUSIONS: Empirical antibiotic treatment of acute cystitis in women without testing for pyuria promotes unnecessary antibiotic use. A simple decision rule provides for prompt treatment of infected women while reducing antibiotic overuse and unnecessary urine testing.
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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 | 0.005 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".