Performance of host-response biomarkers to risk-stratify children with pneumonia in Bhutan
Bibliographic record
Abstract
Pneumonia is the leading cause of post-neonatal death amongst children under five years of age; however, there is no simple triage tool to identify children at risk of progressing to severe and fatal disease. Such a tool could assist for early referral and prioritization of care to improve outcomes and enhance allocation of scarce resources. We compared the performance of inflammatory and endothelial activation markers in addition to clinical signs or scoring scales to risk-stratify children hospitalized with pneumonia at the national referral hospital of Bhutan with the goal of predicting clinical outcome. Of 118 children, 31 evolved to a poor prognosis, defined as either mortality, admission in the paediatric intensive care unit, requirement of chest drainage or requirement of more than five days of oxygen therapy. Soluble triggering receptor expressed on myeloid cells 1 (sTREM-1) was the best performing biomarker and performed better than clinical parameters. sTREM-1 levels upon admission had good predictive accuracy to identify children with pneumonia at risk of poor prognosis. Our findings confirm that immune and endothelial activation markers could be proactively used at first encounter as risk-stratification and clinical decision-making tools in children with pneumonia; however, further external validation is needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".