Heart Rate Assessment Immediately after Birth
Bibliographic record
Abstract
BACKGROUND: Heart rate assessment immediately after birth in newborn infants is critical to the correct guidance of resuscitation efforts. There are disagreements as to the best method to measure heart rate. OBJECTIVE: The aim of this study was to assess different methods of heart rate assessment in newborn infants at birth to determine the fastest and most accurate method. METHODS: PubMed, EMBASE and Google Scholar were systematically searched using the following terms: 'infant', 'heart rate', 'monitoring', 'delivery room', 'resuscitation', 'stethoscope', 'auscultation', 'palpation', 'pulse oximetry', 'electrocardiogram', 'Doppler ultrasound', 'photoplethysmography' and 'wearable sensors'. RESULTS: Eighteen studies were identified that described various methods of heart rate assessment in newborn infants immediately after birth. Studies examining auscultation, palpation, pulse oximetry, electrocardiography and Doppler ultrasound as ways to measure heart rate were included. Heart rate measurements by pulse oximetry are superior to auscultation and palpation, but there is contradictory evidence about its accuracy depending on whether the sensor is connected to the infant or the oximeter first. Several studies indicate that electrocardiogram provides a reliable heart rate faster than pulse oximetry. Doppler ultrasound shows potential for clinical use, however future evidence is needed to support this conclusion. CONCLUSION: Heart rate assessment is important and there are many measurement methods. The accuracy of routinely applied methods varies, with palpation and auscultation being the least accurate and electrocardiogram being the most accurate. More research is needed on Doppler ultrasound before its clinical use.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".