Heparin-Binding Protein Stratifies Mortality Risk Among Ugandan Children Hospitalized With Respiratory Distress
Bibliographic record
Abstract
Abstract Background Current prognostic tools do not reliably and objectively identify children with pneumonia at risk of a severe or life-threatening episode. Heparin-binding protein (HBP) is a host immune protein that is released in response to infection. We hypothesized that measuring HBP concentrations at hospital admission could help risk-stratify children with pneumonia and identify those at higher risk of an adverse prognosis. Methods We evaluated the prognostic accuracy of HBP for predicting in-hospital mortality among children with respiratory distress, and whether HBP could improve the accuracy of validated composite clinical severity scores. Results Of 778 Ugandan children under 5 years of age and presenting with clinically defined pneumonia, 60 (7.7%) died during hospital admission. HBP concentrations at presentation were significantly higher in children with fatal outcomes (median, 76 ng/mL [interquartile range {IQR}, 41–150]) compared to children who survived (median, 31 ng/mL [IQR, 18–57]) (P < .001). Children with HBP >41 ng/mL on admission had an elevated risk of death (hazard ratio, 5.3 [95% confidence interval {CI}, 2.9–9.5]; P < .0001). In receiver operating characteristic (ROC) curve analysis, HBP concentrations distinguished between fatal and nonfatal outcomes (area under the ROC curve, 0.75 [95% CI, .66–.84]) and significantly improved the prediction provided by the Respiratory Index of Severity in Children, a composite clinical severity score (P = .0026). Conclusions Measuring HBP at presentation could help identify children at risk of severe and fatal pneumonia. Adding HBP to clinical scores could improve the recognition and triage of children with pneumonia at risk of death.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".