Do published guidelines predict pneumonia in children presenting to an urban ED?
Bibliographic record
Abstract
OBJECTIVES: In 1997, a Canadian task force published evidence-based guidelines for diagnosing pediatric pneumonia, concluding that the absence of each of four signs (ie, respiratory distress, tachypnea, crackles, and decreased breath sounds) accurately excludes pneumonia. The study was performed to evaluate the accuracy of these guidelines in predicting pneumonia in young children. METHODS: This was an observational study conducted over a 4-month period at an urban emergency department with 80,000 annual visits, approximately 20% of which were children < or =5 years old. Consecutive children < or =5 years old who underwent chest radiography were enrolled. Prior to ordering radiographs, treating physicians were required to enter specific patient signs and symptoms into a computerized database. World Health Organization criteria were used to define tachypnea. Sensitivity, specificity, and predictive values of the task force guidelines in predicting pneumonia were calculated. RESULTS: Three hundred twenty-nine children, including 67 (20%) with pneumonia, were enrolled. Guidelines were 45% sensitive (95% confidence interval (CI) = 33-58) and 66% specific (95% CI = 60-72) for diagnosing pneumonia. Positive and negative predictive values were 25% (95% CI = 18-34) and 82% (95% CI = 77-87), respectively. CONCLUSION: Previously published evidence-based guidelines for excluding pediatric pneumonia were found unreliable in this study.
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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 | 0.004 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".