Initial validation of a method for assessing autonomic nervous system reactivity in epidemiological studies and low resource settings
Bibliographic record
Abstract
The autonomic nervous system (ANS) acts as a central regulator of socioemotional and physiological processes. Individual differences in ANS activity are associated with mental and physical health outcomes across the lifespan, stabilizing over the first 5 years of life. Thus, ANS activity has become a focus of developmental research. Current approaches to assessing ANS are often infeasible for deployment in large epidemiological studies and low resource settings, necessitating new approaches. The goal of the current study was to compare a “gold standard” system (BioRadio) to a new system (Mawi), which offers advantages of ease of use and automated scoring pipelines, in a sample of children in Malawi, an African country with one of the lowest per capita incomes in the world. Twenty-five children, aged 3 to 6 years, were assessed simultaneously with the two systems while participating in a standardized stressor protocol that included baseline, stressor, and recovery periods. Multiple ANS indicators outputted from each system were compared. Analyses assessed agreement in equivalent indices. Absolute heart rate scores were consistently higher from Mawi; consequently, other absolute scores were consistently higher or lower, depending on the index. After adjusting for index score mean differences between systems, agreement was good to excellent (ICC 0.75-0.97). There were no differences between systems in the detection of changes in ANS activity across the baseline, stressor, and recovery episodes of the protocol. The results provide initial evidence that the Mawi system produces data sufficiently similar to that of a gold standard to be considered valid. Thus, the Mawi system may be a feasible option for measuring ANS indicators, with particular utility under certain resource constraints. Future efforts to further validate the Mawi system should include testing in a variety of settings and contexts with larger sample sizes.
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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 | 0.078 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".