Bibliographic record
Abstract
Chronic low back pain (CLBP) is defined as chronic pain in the lower back for three or more consecutive months. Altered sleep architecture is often associated with CLBP and sleep deficiency with concurring side effects can augment pain, further disrupting sleep. The current study aimed to explore whole body heating as a potential therapy for CLBP. Subjects underwent seven consecutive evenings of heating, with completion of the protocol two hours prior to sleep latency onset. Pain was assessed via administration of the McGill Pain Questionnaire prior to the heating protocol (baseline), pre- and post heating on all experimental days and during a 48 hour and two week follow-up. Ability to perform specific tasks was assessed via the functional and symptoms scale, administered at baseline, prior to and following the seven day heating protocol, and during both the follow-up visits. A Sleep Profiler was used throughout the study to measure sleep architecture from the frontal cortex of the brain. A repeated measures ANOVA showed significant decreases in stage N1 sleep (p < 0.05) and increases in N3 sleep (p = 0.001) from baseline to post-treatment. Each subject’s pain perception decreased and allowed for greater functional ability. These findings suggest that seven consecutive evenings of whole body heating protocol altered sleep architecture through the increased amount of restorative stage N3 slow wave, decreased stage N1 sleep, and decreased pain perception. Only small decrements in pain were observed 48 hours and two weeks after the cessation of the intervention.
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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 | 0.000 |
| 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".