Risk Factors For Developing insomnia in Chronic Spinal Pain Patients : a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
ABSTRACTBackground and aims: Insomnia is a major problem in the chronic pain population, including people with chronic spinal pain (CSP), and has a negative impact on health and well-being. The purpose of this systematic review is to identify risk factors for developing insomnia in CSP patients.Methods: Pubmed, Web of Science and Embase were systematically screened for studies encompassing data regarding risk factors of insomnia in people with CSP. The methodological quality of the eligible studies were assessed using the Newcastle-Ottawa Scale.Results: A total of 23 different risk factors were investigated across 8 eligible studies. Of these risk factors, 13 were found to be significant. Two studies demonstrated that people with CSP are more likely to have insomnia when they have high pain intensity levels (3 times more likely), a depression (5 times more likely, pu22640.001), anxiety (3 times more likely, pu22640.005) and/or other comorbidities (2 times more likely, pu22640.005). Pain catastrophizing, using sleep medication, poorer self-rated health, the presence of a neuropathic pain component, having a low income, more outpatient consultations and hospitalizations, not being involved in professional activities, and less physical activity were found to be significant risk factors in only one of the eligible studies. The other investigated risk factors (age, gender, race, BMI, pain duration, spine surgery, shoulder or arm pain, neck mobility problems, myofascial pain, headache) were not found to be significant risk factors.Conclusions: To our knowledge, this is the first review investigating risk factors associated with the development of insomnia in CSP patients. High pain intensity levels, depression, anxiety and the presence of other comorbidities were significantly associated with increased odds for having insomnia.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".