The inverse and non-linear association between central augmentation index and heart rate variability in a cohort of male British combat personnel- findings from the ADVANCE study
Bibliographic record
Abstract
Purpose The central augmentation index (cAIx) is an indirect measure of arterial stiffness. The influence of heart rate variability (HRV) on cAIx remains unexplored in a military cohort and was the aim of this analysis.Method The first follow-up data from the ArmeD serVices trAuma rehabilitatioN outComE (ADVANCE) study were analysed. Participants were male British servicemen who served in Afghanistan (2003-2014) and were divided into two groups at recruitment: injured (who sustained severe combat injury) and uninjured. The uninjured were frequency-matched to the injured by age, rank, role-in-theatre and deployment. HRV was reported as root-mean-square-of-successive-differences (RMSSD) using a five-minute single-lead electrocardiogram. The cAIx was measured using pulse waveform analysis and was adjusted for heart rate at 60 beats/minute (cAIx@60). Effect modification by injury was assessed via interaction analysis. Linear models reported the association between RMSSD (HRV) and cAIx@60 adjusting for a priori confounders.Results 1052 participants (injured n = 526; uninjured 526; median age at follow-up 37.4 years) were examined. Effect modification by injury was not statistically significant; therefore, was adjusted for along with other confounders. RMSSD and cAIx@60 exhibited a moderate inverse correlation (-0.40; p < 0.001). The association between natural log-transformed RMSSD (LnRMSSD) and cAIx@60 was non-linear and statistically significant, suggesting that a 10% decrease in LnRMSSD would be associated with 0.30% increase in cAIx@60.Conclusion Lower RMSSD (HRV) is associated with an increase in cAIx@60, independent of injury status and other traditional cardiovascular risk factors. The efficacy of positive HRV modification on cardiovascular risk in military populations needs to be examined.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".