Do symptoms and initial clinical probability predict the radiological diagnosis of acute sinusitis in children?
Bibliographic record
Abstract
OBJECTIVE: To evaluate the value of signs and symptoms in children for the radiological diagnosis of acute sinusitis. DESIGN: Prospective cohort study. SETTING: University-affiliated tertiary care hospital. PATIENTS: All children presenting with symptoms suggestive of acute sinusitis for whom sinus radiographs were ordered. METHODS: Data were collected on the presence of specific symptoms and the initial probability of sinusitis. Criterion-based radiological diagnoses were made. RESULTS: Three hundred ninety-two consecutive children were seen; 257 children had a radiological diagnosis of acute sinusitis (66%), 128 patients (33%) presented with complete opacity of at least one sinus and 14 (4%) children had an air-fluid level. Sensitivity, specificity, predictive values and likelihood ratios were measured for clinical findings. Classical symptoms (rhinorrhea lasting more than 10 days and purulent rhinorrhea) increased the likelihood ratios the most (1.3 and 1.34, respectively). Logistic regression showed two independent predictors: purulent rhinorrhea (odds ratio 2.0) and the presence of acute otitis media (odds ratio 2.6). The initial clinical probability was more accurate than any other single finding: high probability (likelihood ratio 2.0), intermediate probability (likelihood ratio 1.1) and low probability (likelihood ratio 0.6). CONCLUSION: Classical symptoms are predictive of the presence of acute sinusitis as diagnosed on sinus radiographs. The physician's overall clinical impression, expressed as an initial probability, was superior to any single historical or examination finding in the diagnosis of acute sinusitis.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".