The Association Between Chronic Low Back Pain and Sleep
Bibliographic record
Abstract
OBJECTIVES: chronic low back pain (CLBP) adversely affects many quality of life components, and is reported to impair sleep. The aim of this review was to determine the association between CLBP and sleep. METHODS: this review comprised 3 phases: an electronic database search (PubMed, Cinahl Plus, EMBASE, PsychInfo, Pedro, and Cochrane Library) identified potential articles; these were screened for inclusion criteria by 2 independent reviewers; extraction of data from accepted articles; and rating of internal validity by 2 independent reviewers and strength of the evidence using valid and reliable scales. RESULTS: the search generated 17 articles that fulfilled the inclusion criteria (quantitative n=14 and qualitative n=3). CLBP was found to relate to several dimensions of sleep including: sleep disturbance and duration (n=15), sleep affecting day-time function (n=5), sleep quality (n=4), sleep satisfaction and distress (n=4), sleep efficiency (n=4), ability to fall asleep (n=3), and activity during sleep (n=3). Consistent evidence found that CLBP was associated with greater sleep disturbance; reduced sleep duration and sleep quality; increased time taken to fall asleep; poor day-time function; and greater sleep dissatisfaction and distress. Inconsistent evidence was found that sleep efficiency and activity were adversely associated with CLBP. DISCUSSION: many dimensions of sleep are adversely associated with CLBP. Management strategies for CLBP need to address these to maximize quality of life in this patient cohort.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".