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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".