Diagnostic Performance Analysis of the Point-of-Care Bilistick System in Identifying Severe Neonatal Hyperbilirubinemia by a Multi-Country Approach
Bibliographic record
Abstract
IMPORTANCE: The real prevalence and clinical burden of severe neonatal jaundice are undefined due to difficulties in measuring total serum bilirubin (TSB) outside secondary and tertiary clinical centers. OBJECTIVE: To assess the diagnostic performance of the point-of care Bilistick System (BS) in identifying neonatal jaundice patients requiring treatment. DESIGN: Between April 2015 and November 2016, 1911 neonates, were recruited to participate in the study. Blood samples were simultaneously collected for the TSB determination by BS and by hospital laboratory (Lab). Data were collected and sent to the Bilimetrix headquarter in Trieste where statistical analysis was performed. Newborns with neonatal jaundice were treated with phototherapy according to each center's guidelines. SETTING: 17 hospitals from Nigeria, Egypt, Indonesia, and Viet Nam. PARTICIPANTS: 1911 newborns were included, of which 1458 (76·3%) fulfilled the inclusion criteria. RESULTS: TSB level measured by BS agreed (p < .0001) with the lab result in all four countries. The diagnostic performance of BS showed a positive predictive value (PPV) of 92·5% and a negative predictive value (NPV) of 92·8%. CONCLUSIONS AND RELEVANCE: BS is a reliable system to detect neonatal jaundice over a wide range of bilirubin levels. Since Bilistick is a point-of-care test, its use may provide appropriate and timely identification of jaundiced newborns requiring treatment.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".