Development and validation of a simple algorithm for initiation of CPAP in neonates with respiratory distress in Malawi
Bibliographic record
Abstract
BACKGROUND: Low-cost bubble continuous positive airway pressure (bCPAP) systems have been shown to improve survival in neonates with respiratory distress, in developing countries including Malawi. District hospitals in Malawi implementing CPAP requested simple and reliable guidelines to enable healthcare workers with basic skills and minimal training to determine when treatment with CPAP is necessary. We developed and validated TRY (T: Tone is good, R: Respiratory Distress and Y=Yes) CPAP, a simple algorithm to identify neonates with respiratory distress who would benefit from CPAP. OBJECTIVE: To validate the TRY CPAP algorithm for neonates with respiratory distress in a low-resource setting. METHODS: We constructed an algorithm using a combination of vital signs, tone and birth weight to determine the need for CPAP in neonates with respiratory distress. Neonates admitted to the neonatal ward of Queen Elizabeth Central Hospital, in Blantyre, Malawi, were assessed in a prospective, cross-sectional study. Nurses and paediatricians-in-training assessed neonates to determine whether they required CPAP using the TRY CPAP algorithm. To establish the accuracy of the TRY CPAP algorithm in evaluating the need for CPAP, their assessment was compared with the decision of a neonatologist blinded to the TRY CPAP algorithm findings. RESULTS: 325 neonates were evaluated over a 2-month period; 13% were deemed to require CPAP by the neonatologist. The inter-rater reliability with the algorithm was 0.90 for nurses and 0.97 for paediatricians-in-training using the neonatologist's assessment as the reference standard. CONCLUSIONS: The TRY CPAP algorithm has the potential to be a simple and reliable tool to assist nurses and clinicians in identifying neonates who require treatment with CPAP in low-resource settings.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".