Fast and accurate newborn heart rate monitoring at birth: A systematic review
Bibliographic record
Abstract
Aim: To examine speed and accuracy of newborn heart rate measurement by various assessment methods employed at birth. Methods: A search of Medline, SCOPUS, CINAHL and Cochrane was conducted between January 1, 1946, to until August 16, 2023. (CRD 42021283364) Study selection was based on predetermined criteria. Reviewers independently extracted data, appraised risk of bias and assessed certainty of evidence. Results: Pulse oximetry is slower and less precise than ECG for heart rate assessment. Both auscultation and palpation are imprecise for heart rate assessment. Other devices such as digital stethoscope, Doppler ultrasound, an ECG device using dry electrodes incorporated in a belt, photoplethysmography and electromyography are studied in small numbers of newborns and data are not available for extremely preterm or bradycardic newborns receiving resuscitation. Digital stethoscope is fast and accurate. Doppler ultrasound and dry electrode ECG in a belt are fast, accurate and precise when compared to conventional ECG with gel adhesive electrodes. Limitations: Certainty of evidence was low or very low for most comparisons. Conclusion: If resources permit, ECG should be used for fast and accurate heart rate assessment at birth. Pulse oximetry and auscultation may be reasonable alternatives but have limitations. Digital stethoscope, doppler ultrasound and dry electrode ECG show promise but need further study.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".