Effect Sizes for Changes in Health and Well-Being Following Treatment With the One-To-Zero Technique in Individuals With Occipito-Atlantal Joint Dysfunction: A Repeated Measures Study
Bibliographic record
Abstract
Objectives The purpose of this study was to determine effect sizes (ES) for changes in self-reported measures of musculoskeletal pain and dysfunction resulting from the one-to-zero method using a repeated measures study design. Methods Twenty participants presenting with articular dysfunction of the occipito-atlantal (C0-C1) complex were treated using the one-to-zero method, a high-velocity low-amplitude thrust administered between the C0-C1 complex before treating other restrictive segments in a cephalocaudal direction. The participants completed online questionnaires using Google Forms that assessed aspects of the biopsychosocial model of pain at baseline and within a week after treatment. The questionnaires included the following: (1) Demographic and Health Behavior Survey; (2) Neck Bournemouth Questionnaire (NBQ) or Neck Disability Index (NDI); (3) Beck Anxiety Index (BAI); (4) Insomnia Severity Index (ISI); and (5) 36-Item Short Form Health Survey (SF-36). Paired t test or Wilcoxon signed ranks test was performed, dependent on normality. Cohen's d values were calculated for each questionnaire score (0.20 indicative of small; ≥0.50 medium; and ≥0.80 large ES). Results The NDI, NBQ, BAI, and ISI had a large ES (all d ≥ 0.80). In the SF-36, 4 subscales had a small to near-medium ES, 1 subscale had a medium to near-large ES, and the remaining 2 had a large ES ( d ≥ 0.80). The physical and mental component summary had a large ( d = 0.88) and small ES ( d = 0.35), respectively. Conclusion The effect sizes suggest the one-to-zero treatment induces change in various aspects of the biopsychosocial model .
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".