Central post-stroke pain: predictors and relationship with magnetic resonance imaging and somatosensory evoked potentials
Bibliographic record
Abstract
Central post-stroke pain (CPSP) is an under-recognized complication of stroke although it can lead to deterioration in quality of life and impairment in activities of daily living. Its estimated prevalence varies between 18.6 and 49%. To investigate the prevalence and predictors of CPSP in ischemic stroke patients and to find its relationship with somatosensory evoked potentials (SSEPs) and magnetic resonance imaging. Sixty five consecutive patients with recent first attack of ischemic stroke who were admitted to the Neurology Department, Suez Canal University Hospitals were recruited. Patients were subjected to clinical assessment, Hamilton depression rating scale, brain MRI, short-form McGill Pain Questionnaire (SF-MPQ), daily pain rating scale (DPRS), stimulus evoked pain, and SSEPs. The total prevalence rate of CPSP was 35.4% ( n = 23). The mean age of the patients developed CPSP was significantly lower than those without CPSP ( p = 0.004). Deep sensory dysfunction was statistically significantly higher among CPSP group than non-CPSP group ( p = 0.001). CPSP group showed statistically significant higher prevalence of thalamic stroke ( p = 0.007), as well as significant abnormalities in inter-peak interval (IPL) of median and tibial nerves SSEPs ( p < 0.05). Thalamic group showed higher abnormalities in IPL of median and tibial nerves compared to extra-thalamic group, but without statistically differences. The prevalence of CPSP was found to be 35.4%. Predictors of CPSP include; deep sensory dysfunction, prolongation of tibial N21–P40 IPL, smoking history, age < 50 years, presence of thalamic stroke and prolongation of median N9–N20 IPL.
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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 | 0.001 |
| 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) | 0.002 | 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".