External validation study of a clinical decision aid to reduce unnecessary antibiotic prescriptions in women with acute cystitis
Bibliographic record
Abstract
BACKGROUND: Empirical prescribing of antibiotics to women with symptoms of acute cystitis prior to culture results is common, but subsequent culture results are often negative. A clinical decision aid for prescribing decisions in acute cystitis was previously developed that could reduce these unnecessary antibiotic prescriptions but has not been validated. This study sought to validate this decision aid for empirical antibiotic prescribing decisions in a new cohort of women with suspected acute cystitis. METHODS: CFU/L)) was determined, and compared with physician management, and the earlier development cohort study estimates. Other outcomes assessed were total antibiotic prescriptions, unnecessary antibiotics for negative urine cultures, and recommendations for urine culture testing. Chi-square tests were used for unpaired comparisons, adjusted for physician clustering. McNemar's test was used for paired comparisons. RESULTS: There were 245/397 (61.7%) positive urine cultures. The cystitis aid sensitivity was 202/245 (82.5%, 95% Confidence Interval (CI)) = 77.1%, 86.8%), compared to 167/208 (80.3%) in the previous development cohort (p = 0.54), and 239/245 (97.6%) by family physicians in the current study (p < 0.001). Specificity was low for physicians (10/152, 6.6%) compared to the decision aid (54/152, 35.5%; p < 0.001, resulting in more antibiotic prescriptions by physicians (381/397, 96.0%) than would occur with decision aid recommendations (300/397, 75.6%, p < 0.001). Unnecessary antibiotic prescriptions where urine cultures were negative would be reduced an absolute 11.1% with cystitis aid recommendations (98/397, 24.7%) compared to usual physician care (142/397, 35.8%; p = 0.001). Urine cultures would also be reduced (97/397, 24.4% decision aid vs 351/397, 88.4% physicians; p < 0.001). CONCLUSIONS: A 3-item clinical decision aid demonstrated reproducible accuracy in two cohorts of women with acute cystitis symptoms. Clinically important reductions in total and unnecessary antibiotic use, as well as urine culture testing, could result with routine clinical use compared to current empirical physician management practices.
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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 | 0.047 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".