Prevalence of chronic pain among individuals with neurological conditions.
Bibliographic record
Abstract
BACKGROUND: The prevalence of pain among people with a variety of individual neurological conditions has been estimated. However, information is limited about chronic pain among people with neurological conditions overall, and about the conditions for which chronic pain is most prevalent. To fill these information gaps, a common method of pain assessment is required. DATA AND METHODS: The data are from the Survey on Living with Neurological Conditions in Canada, a cross-sectional national survey. Based on self-reports, chronic pain was assessed for 16 neurological conditions. Multivariable logistic regression was used to produce odds ratios and 95% confidence intervals (CIs). RESULTS: Close to 1.5 million individuals aged 15 or older who lived in private households reported having been diagnosed with a neurological condition. The overall prevalence of chronic pain for the 16 neurological conditions combined was 36% (95% CI: 31% to 42%). The odds of chronic pain were significantly elevated among individuals with spinal cord trauma. DISCUSSION: Chronic pain is highly prevalent among people with neurological conditions, particularly those with spinal cord trauma. These results suggest a need to target health services and direct research to improved pain management, and thereby reduce the burden of neurological disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".