Chronic Pain Syndromes After Ischemic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Chronic pain syndromes are reported to be common after stroke, but most previous epidemiological studies have generally included small cohorts of patients with relatively short-term follow-up. In a large cohort with ischemic stroke (Prevention Regimen for Effectively avoiding Second Stroke [PRoFESS] trial), we determined the prevalence, risk factors, and clinical consequence of new poststroke pain syndromes. METHODS: Within the PRoFESS trial (mean follow-up 2.5 years), a standardized chronic pain questionnaire was administered (at the penultimate follow-up visit) to all participants who reported chronic pain since their stroke and did not have a history of chronic pain before their index stroke. Multivariable logistic regression analyses were used to determine risk factors for poststroke pain (and pain subtypes), and the association between poststroke pain and cognitive (≥ 3 reduction in Mini-Mental State Examination score) and functional decline (≥ 1 increase in m-Rankin). RESULTS: In total, 15 754 participants were included; of which 1665 participants (10.6%) reported new chronic poststroke pain, and included 431 participants (2.7%) with central poststroke pain, 238 (1.5%) with peripheral neuropathic pain, 208 (1.3%) with pain from spasticity, and 136 participants (0.9%) with pain from shoulder subluxation. More than 1 pain subtype was reported in 86 participants (0.6%). Predictors of poststroke pain included increased stroke severity, female sex, alcohol intake, statin use, depressive symptoms, diabetes mellitus, antithrombotic regimen, and peripheral vascular disease. A new chronic pain syndrome was associated with greater dependence (odds ratio, 2.16; 95% confidence interval, 1.82-2.56). Peripheral neuropathy and pain from spasticity/shoulder subluxation were associated with cognitive decline. CONCLUSIONS: Chronic pain syndromes are common after ischemic stroke and are associated with increased functional dependence and cognitive decline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".