Validation of a Decision Aid to Assist Physicians in Reducing Unnecessary Antibiotic Drug Use for Acute Cystitis
Bibliographic record
Abstract
BACKGROUND: In a previous study, use of a decision aid based on 4 clinical items would have reduced unnecessary antibiotic prescriptions for acute cystitis by 30% compared with usual physician care. METHODS: We assessed the decision aid in a different population of females seen in community-based practice. Between April 7, 2002, and March 20, 2003, 225 Canadian family physicians recorded clinical findings, urine dip test results, and treatment decisions for 331 females with suspected cystitis. The number of decision aid items present was determined for each patient, and the sensitivity and specificity of decision aid recommendations for empirical antibiotics were determined using the gold standard of a positive urine culture result (> or =10(2) colony-forming units per milliliter). Total antibiotic prescriptions, unnecessary prescriptions (for negative culture results), and recommendations for urine cultures were determined and compared with physician management. RESULTS: Three of the original decision aid variables (dysuria, the presence of leukocytes [greater than a trace amount], and the presence of nitrites [any positive]) were associated with having a positive urine culture result (P < or = .001), but 1 variable (symptoms for 1 day) was not (P = .96). A simplified decision aid incorporating the 3 significant variables (empirical antibiotics without culture if > or =2 variables present; otherwise obtain a culture and wait for results) had a sensitivity of 80.3% (167/208) and a specificity of 53.7% (66/123). Following decision aid recommendations would have reduced antibiotic prescriptions by 23.5%, unnecessary prescriptions by 40.2%, and urine cultures by 59.0% compared with physician care (P < .001 for all). CONCLUSION: A simple 3-item decision aid could significantly reduce unnecessary antibiotic drug prescriptions and urine culture testing in females with symptoms of acute cystitis.
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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 | 0.012 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".