A cost effectiveness analysis of omitting radiography in diagnosis of acute bronchiolitis
Bibliographic record
Abstract
OBJECTIVE: To carry out a cost-effectiveness analysis of omitting chest radiography in the diagnosis of infant bronchiolitis. HYPOTHESIS: Omitting chest radiographs in the diagnosis of typical bronchiolitis was expected to reduce costs without adversely affecting the detection rate of alternate diseases. STUDY DESIGN: An economic evaluation was conducted using clinical and health resources. Emergency department (ED) physicians provided diagnoses pre- and post-radiography as well as a management plan. The primary outcome was the diagnostic accuracy (false-negative rate) of alternate diagnoses with and without X-ray. The incremental costs of omitting radiography in comparison to routine radiography per patient were assessed from a health system perspective. PATIENT SELECTION: We studied 265 infants, 2-23 months old, presenting at the ED with typical bronchiolitis. Patients with pre-existing conditions or radiographs were omitted from the study. METHODOLOGY: Expected costs to the health care system of including and excluding chest radiographs were compared, including costs associated with misdiagnosis. RESULTS: All alternate diagnoses (two cases) were missed by ED physicians pre- and post-radiography, resulting in a 100% false negative rate. The specificity in detecting alternate diseases was 96.6% pre-radiography and 88.6% post-radiography. Of the 17 cases of coexistent pneumonia, 88% were missed pre-radiography and 59% post-radiography, with respective false positive rates of 10.5% and 16.1%. Omission of routine chest radiograph saved CDN $59 per patient, primarily due to savings in radiography and hospitalization costs. The economic benefit persisted after the inpatient length of stay, ED overhead and radiograph costs were varied. CONCLUSION: For infants with typical bronchiolitis, omitting radiography is cost saving without compromising diagnostic accuracy of alternate diagnoses and of associated pneumonia.
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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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".