Patterns of heart rate variability (HRV) responses to acute stress in neonates: A systematic review
Bibliographic record
Abstract
eview question / Objective The overall objective of this systematic review is to synthesize the literature examining preterm and full-term newborns' acute heart rate variability (HRV) responses to painful and non-painful disruptive procedures.It will address the following research questions: 1) What patterns of HRV responses do newborns exhibit during acute painful and non-painful disruptive procedures?2) How does procedural pain severity influence neonatal HRV response patterns during acute painful procedures?Rationale Early neonatal exposure to repeated stressors, such as disruptive medical procedures in hospital settings, has been linked to impaired a u t o n o m i c n e r v o u s s y s t e m ( A N S ) s t re s s responses.Furthermore, other clinical variables, such as prematurity, have been associated with an underdeveloped ANS, increasing the vulnerability of hospitalized newborns to stress exposure.Heart rate variability (HRV) measures capture the balance of both branches of the ANS (sympathetic nervous system [SNS] and parasympathetic nervous system [PNS]), thus providing a non-invasive measure of stress reactivity and recovery.However, in newborns, heart rate has been more widely used to index cardiac stress responding to acute stress and pain in clinical settings.Although HRV measures are widely used to capture stress
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".