Mitigating Bias in the Measurement of Heart Rate Variability in Physiological Studies of Spinal Manipulation: A Comparison Between Authentic and Sham Manipulation
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to identify sources and strategies for the mitigation of bias in studies of spinal manipulation and heart rate variability. METHODS: A small-scale study compared the effects of a single session of sham and authentic cervical manipulation on heart rate variability as measured by power spectrum analysis. The participants were a sample of 31 healthy young students from the Canadian Memorial Chiropractic College, randomized into 2 study arms. The effectiveness of blinding was evaluated, and 2 alternative methods of data analysis were explored to mitigate risk of bias. Following execution of the study, the stages of implementation and data processing were scored against version 2 of the Cochrane risk-of-bias tool for randomized trials for risk of bias. RESULTS: The risk of bias arising from (1) the randomization process, (2) missing outcome data, and (3) selection of reported results was judged to be low. Risk of bias in (1) deviations from intended interventions (particularly due to the failure of masking) and (2) the measurement of the outcome, for example, through cleaning of the data, were judged to be high. CONCLUSION: The use of power spectrum analysis of heart rate variability based on 5-minute recordings of echocardiogram pre-and post-intervention contained multiple sources of bias that were challenging to mitigate. Based upon these findings, power spectrum analysis of heart rate variability using these parameters may be ill-suited to the study of physiological effects of spinal manipulative therapy.
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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 | 0.269 | 0.418 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".