Overestimation Error and Unnecessary Antibiotic Prescriptions for Acute Cystitis in Adult Women
Bibliographic record
Abstract
BACKGROUND: Empiric antibiotic prescribing for suspected acute cystitis may lead to unnecessary prescriptions when urine cultures are negative. This study assessed whether physician overestimation of the likelihood of bacterial infection contributed to unnecessary antibiotic prescriptions. METHODS: This was a cross-sectional study in Toronto, Canada, from 1998 to 2000 of 231 women 16 years and older who underwent standardized clinical assessments and urine culture testing. The main outcome was an unnecessary antibiotic prescription, defined as a prescription where the urine culture was negative. The difference between physician estimates of the likelihood of a positive urine culture and the measured culture rate for women with similar symptoms was used to measure overestimation error. Logistic regression was used to assess associations between unnecessary prescriptions and clinical factors or overestimation error. Multiple logistic regression was used to adjust for the effect of clinical factors. RESULTS: Of 230 women assessed, 186 (80.9%) were prescribed antibiotics and 74 (32.2%) were prescribed an unnecessary antibiotic where the urine culture was negative. When an overestimation error above the median value (14.75%) was present, the odds of an unnecessary antibiotic prescription were increased (adjusted odds ratio = 3.72; 95% confidence interval = 1.75-7.89). A high overestimation error was associated with the symptoms of urinary frequency or suprapubic tenderness and costovertebral angle tenderness on examination. CONCLUSIONS: Physician overestimation of the likelihood of a positive urine culture in women with symptoms of acute cystitis was associated with unnecessary antibiotic prescribing. Antibiotic overuse may be reduced by developing treatment strategies that deemphasize nonspecific clinical findings that contribute to physician overestimation error.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".