Prevalence, Trajectory, and Predictors of Poststroke Pain: Retrospective Analysis of Pooled Clinical Trial Data Set
Bibliographic record
Abstract
BACKGROUND: Poststroke pain remains underdiagnosed and inadequately managed. To inform the optimum time to initiate interventions, we examined prevalence, trajectory, and participant factors associated with poststroke pain. METHODS: Eligible studies from the VISTA (Virtual International Stroke Trials Archives) included an assessment of pain. Analyses of individual participant data examined demography, pain, mobility, independence, language, anxiety/depression, and vitality. Pain assessments were standardized to the European Quality of Life Scale (European Quality of Life 5 Dimensions 3 Level) pain domain, describing no, moderate, or extreme pain. We described pain prevalence, associations between participant characteristics, and pain using multivariable models. RESULTS: From 94 studies (n>48 000 individual participant data) in VISTA, 10 (n=10 002 individual participant data) included a pain assessment. Median age was 70.0 years (interquartile range [59.0-77.1]), 5560 (55.6%) were male, baseline stroke severity was National Institutes of Health Stroke Scale score 10 (interquartile range [7-15]). Reports of extreme pain ranged between 3% and 9.5% and were highest beyond 2 years poststroke (31/328 [9.5%]); pain trajectory varied by study. Poorer independence was significantly associated with presence of moderate or extreme pain (5 weeks-3 months odds ratio [OR], 1.5 [95% CI, 1.4-1.6]; 4-6 months OR, 1.7 [95% CI, 1.3-2.1]; >6 months OR, 1.5 [95% CI, 1.2-2.0]), and increased severity of pain (5 weeks-3 months: OR, 1.2 [95% CI, 1.1-1.2]; 4-6 months OR, 1.1 [95% CI, 1.1-1.2]; >6 months, OR, 1.2 [95% CI, 1.1-1.2]), after adjusting for covariates. Anxiety/depression and lower vitality were each associated with pain severity. CONCLUSIONS: Between 3% and 9.5% of participants reported extreme poststroke pain; the presence and severity of pain were independently associated with dependence at each time point. Future studies could determine whether and when interventions may reduce the prevalence and severity of poststroke pain.
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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 | 0.047 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".