Evaluation of a Tap-Based Smartphone App for Heart Rate Assessment During Asphyxia in a Porcine Model of Neonatal Resuscitation
Bibliographic record
Abstract
Objectives: Heart rate (HR) is the most significant parameter to assess a newborn’s clinical status at birth. Recently, novel technologies including smartphone applications have been suggested for HR assessment during neonatal resuscitation. The aim of this study was to evaluate the accuracy, speed, and precision of the NeoTapLifeSupport (NeoTapLS) smartphone application using a digital stethoscope (DS) for HR assessment during neonatal resuscitation. Design: Newborn piglets (n=20, 1-3 days, 1.7-2.4kg) were anaesthetised, intubated, mechanically ventilated, and subjected to 30 min of hypoxia, followed by asphyxia. Asphyxia was induced by clamping the endotracheal tube and disconnecting the ventilator, until asystole was confirmed by zero carotid blood flow (CBF). Setting: Experimental setting. Subjects: Asphyxia-induced newborn piglets. Interventions: During asphyxia, HR assessments were performed with a DS using the NeoTapLS smartphone application, and compared to 6-sec method (6sec), and 10-sec method (10sec). Measurements and Main Results: Accuracy of obtained HRs was compared to CBF and electrocardiogram and assessment time using NeoTapLS, 6sec, and 10sec were also measured. The mean(SD) HR with the NeoTapLS was 68(26), compared to CBF with 68(27) bpm, 6sec with 68(27), and 10sec with 66(26) bpm during asphyxia. Bland-Altman analysis revealed no difference between HR using the NeoTapLS, 6sec, 10sec, compared to CBF HR, with NeoTapLS showing the smallest difference between 95% limits of agreement. The median (IQR) time required to obtain a HR using the NeoTapLS was 3(2-4)sec, compared to 6(6-7) and 10(10-11) sec for 6sec and 10sec, respectively. Conclusions Our data suggests that the NeoTapLS is accurate, fast, and precise during neonatal asphyxia to assess heart rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".