Does Clinical Error Contribute to Unnecessary Antibiotic Use?
Bibliographic record
Abstract
Patient expectations and physician attitudes are often cited as factors in the overuse of antibiotics. This study examined whether clinical error might also be important. In treating 517 patients with sore throat, family physicians estimated the probability that group A streptococcus infection was present. Two thirds of antibiotics prescribed were to culture-negative patients and therefore considered unnecessary. Physicians overestimated the probability that a group A streptococcal infection was present by an average 33.2% in these cases, compared with 6.9% otherwise (p < 0.001). The rate of unnecessary prescribing was 5.1% when the physician estimate differed from the true probability of a group A streptococcal infection by <10%, 16.0% for an error of 10-29%, 35.6% for an error of 30-49%, and 78.3% when the chance of the infection was overestimated by 50% or more. Clinical error in estimating the likelihood of group A streptococcal infection probably contributes to unnecessary antibiotic use in patients with sore throat.
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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 | 0.030 | 0.410 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".