Effectiveness of a selected bedding system on quality of sleep, low back pain, shoulder pain, and spine stiffness
Bibliographic record
Abstract
OBJECTIVE: To determine whether clinical and statistically significant changes in back pain, shoulder pain, spine stiffness, and quality of sleep may be documented after use of a prescribed bedding system. DESIGN: Quasi-experimental field study of single group pretest-posttest design with subjects serving as their own controls. SETTING: Two chiropractic clinics and the Oklahoma State University Program of Health and Human Performance. SUBJECTS: Convenience sample of 22 subjects (women, n = 13; men, n = 9) between the ages of 25 and 75 years with documented disturbed sleep, shoulder pain, low back pain, and spine stiffness of a chronic nature. OUTCOME MEASURES: Pretest and posttest 28-day Visual Analog Scales for pain, spine stiffness, and quality of sleep. MAIN RESULTS: The experimental bedding system reduced back pain by 57.21% (P =.000001), reduced shoulder pain by 60.83% (P =.000005), reduced back stiffness by 59.12% (P =.000004), and improved quality of sleep by 60.73% (P =.000001). CONCLUSIONS: Results suggest that subjects obtain significant improvement in shoulder and back pain, back stiffness, and quality of sleep after 28 days of prescribed bedding system use as compared with 28 days of personal bedding use. Female subjects and those with lower body weight were more likely to significantly improve than heavier and more obese subjects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".