Three-Dimensional Spinal Position With and Without Manual Distraction Load Increases Spinal Height
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate if spinal height increases using 3-dimensional (3-D) spinal position with and without manual distraction load and to assess the correlation between spine height changes and degrees of trunk rotation. METHODS: Fifty-six participants were randomly placed in one of two groups: (1) 3-D spinal position with manual distraction load, and (2) without manual distraction load. Spinal height was measured before and after the interventions using a stadiometer. For the statistical analysis, we used a 2 (Loading status: pre- versus post-intervention height) X 2 (3-D spinal position: with versus without manual distraction load) repeated measures Analysis of Variance (ANOVA) was used to identify significant interaction and main effects. Paired t-tests were used to calculate differences in spinal height changes between the two interventions. Pearson correlation coefficient was used to measure correlations between changes in spinal heights and degrees of trunk rotation. RESULTS: Mean spinal height increase with 3-D spinal position with and without manual distraction load was 6.30 mm (±6.22) and 5.69 mm (±4.13), respectively. No significant interaction effect was present between loading status and 3-D spinal position but a significant main effect in loading status was. Paired t-tests revealed significant differences in spinal heights between pre-and post-3-D spinal position with and without manual distraction load. No significant correlation was measured between trunk rotation and spinal height changes. CONCLUSION: 3-D spinal position with or without distraction load increased spinal height. This suggests that 3-D spinal positioning without manual distraction could be used in home settings to help maintain intervertebral disc (IVD) health.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".