Can We Predict Which Children With Clinically Suspected Pneumonia Will Have the Presence of Focal Infiltrates on Chest Radiographs?
Bibliographic record
Abstract
OBJECTIVE: To determine predictive factors for the presence of focal infiltrates in children with clinically suspected pneumonia in a pediatric emergency department. METHODS: Children (1-16 years) with clinically suspected pneumonia were studied prospectively. The presenting features were compared between the children with and without focal infiltrates using chi2 analysis, t test, and odds ratio with 95% confidence intervals. A multivariate prediction rule was developed using logistic regression. RESULTS: A total of 570 were studied. Risk factors (odds ratio; 95% confidence interval) for the presence of focal infiltrates included history of fever (3.1; 1.7-5.3), decreased breath sounds (1.4; 1.0-2.0), crackles (2.0; 1.4-2.9), retractions (2.8; 1.0-7.6), grunting (7.3; 1.1-48.1), fever (1.5; 1.2-1.9), tachypnea (1.8; 1.3-2.5), and tachycardia (1.3; 1.0-1.6). We then used logistic regression to develop a candidate prediction rule for the variables of fever, decreased breath sounds, crackles, and tachypnea, which had an area under the receiver operating curve of 0.668. This rule had excellent sensitivity (93.1%-98%) yet poor specificity (5.7%-19.4%). CONCLUSIONS: Multiple predictive factors for children with suspected pneumonia have been identified. Patients with focal infiltrates were more likely in our study to have a history of fever, tachypnea, increased heart rate, retractions, grunting, crackles, or decreased breath sounds. A multivariate prediction rule shows promise for the accurate prediction of pneumonia in children. However, the prospective evaluation of this multivariate prediction rule in a clinical setting is still required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".