The impact of diagnostic uncertainty on antibiotic prescribing for pediatric respiratory tract infection
Bibliographic record
Abstract
Antibiotic misuse for viral upper respiratory tract infections (URI) in children is a significant public health problem. We determined the influence of clinical features (age, general appearance, fever), which increase diagnostic uncertainty in children with URI, on antibiotic prescribing for URI using clinical judgment analysis. We created 16 scenarios of children with URI and distributed them to 540 pediatricians and family practitioners in Ontario by mail survey. Logistic regression was used to determine the association of these and demographic variables with antibiotic prescribing. Two hundred fifty-seven physicians responded. Poor general appearance, fever above 38.5°C and age over 2 years were associated with prescribing, whereas, parental pressure for an antibiotic was not. Physician demographic characteristics associated with antibiotic use were: family practitioner, female physician, larger number of patients seen per week and increased physician age. Thus, clinical factors, suggestive of more serious infection, contribute to diagnostic uncertainty and are associated with antibiotic use for pediatric URI.
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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 | 0.002 | 0.037 |
| 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 | 0.001 | 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 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".