Heart Rate Variability Modulation Produced by a Chiropractic Lumbar Adjustment
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the heart rate variability (HRV) in the presence or the absence of pain in the lower back (L5), while receiving chiropractic care. METHODS: A total of 53 healthy subjects were randomly assigned to a control, 2 treatment or 2 sham groups (n = 10 per group). Subjects underwent an eight-minute acclimatizing period. The HRV tachygram (RR interval) data were recorded directly into a Suunto watch (model T6). We analyzed the five minute pre-treatment and post treatments intervals. The spectral analysis of the tachygram was performed with the Kubios Software (University of Kuopio, Finland). RESULTS: The HF component (0.15-0.40 Hz) in the pain-free group decreased significantly (p<0.05) from (Pre-treatment) 29.26 ± 17.16 Hz to (Post-treatment) 24.95 ± 19.73 Hz, and in the pain group increased significantly from 22.84 ± 16.7 Hz to 23.64 ± 19.44 Hz. The Mean R-R intervals in the pain-free grouping increased significantly from 822.32 ± 127.29 ms to 830.25 ± 127.82 ms and finally the VLF (0.0-0.04 Hz) of the pain grouping increased significantly from 46.71 ± 23.36 Hz to 49.43 ± 22.55 Hz. CONCLUSIONS: We found that pain is a factor in the directional change of the HF component of the HRV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".