Handheld Point-of-Care Lactate Measurement at Admission Predicts Mortality in Ugandan Children Hospitalized with Pneumonia: A Prospective Cohort Study
Bibliographic record
Abstract
Globally, pneumonia is the leading cause of death among children younger than 5 years old, with most deaths occurring in low-income countries. Rapid bedside tools to assist practitioners to accurately triage and risk-stratify these patients may improve clinical care and patient outcomes. We conducted a prospective cohort study of children with pneumonia admitted to two Ugandan hospitals to examine the predictive value of a single point-of-care lactate measurement using a commercially available handheld device, the Lactate Scout Analyzer. One hundred and fifty-five children were included, 90 (58%) male, with a median (interquartile range [IQR]) age of 11 (1.4-20) months. One hundred and twenty-five (81%) patients had chest indrawing, 133 (86%) were hypoxemic, and 75 (68%) had a chest x-ray abnormality. In-hospital mortality was 22/155 (14%). Median (IQR) admission lactate level was 2.4 (1.8-3.6) mmol/L among children who survived versus 7.2 (2.6-9.7) mmol/L among those who died (P 4.0 mmol/L accurately risk-stratified children, with 5-day mortality of 2%, 11% and 26%, respectively (P < 0.001). Slow lactate clearance also predicted subsequent mortality in children with repeated lactate measurements. Hand-held lactate measurement is a clinically informative and convenient tool in low-resource settings for triage and risk stratification of pediatric pneumonia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".