Classifying post-stroke shoulder pain: Can the DN4 be helpful?
Bibliographic record
Abstract
The etiology of post-stroke shoulder pain (PSSP) is largely unclear and may involve both nociceptive and neuropathic mechanisms. No gold standard is present for PSSP diagnosis. The neuropathic pain diagnostic questionnaire (DN4), was originally developed to identify neuropathic pain in the clinical context. In this study we used the DN4 to categorize PSSP patients and compared symptoms and signs suggestive of either nociceptive or neuropathic pain. Pain complaints and sensory functions were compared between patients with chronic PSSP scoring at least four (DN4+, n=9) or less than four (DN4-, n=10) on the DN4. Pain was assessed using a numeric rating scale and the McGill pain questionnaire. Sensory functions were assessed using clinical examination and quantitative sensory testing combined with a cold pressor test. Patients classified as DN4+ reported constant pain, higher pain intensity, a higher impact of pain on daily living, more frequent loss of cold sensation, reduced QST thresholds at the unaffected side and increased QST thresholds at the affected side. Notably, several symptoms and signs suggestive of either neuropathic or nociceptive pain corresponded to the subgroups DN4+ and DN4- respectively. However, since the pathophysiological mechanisms remain unclear and none of the sensory signs could be exclusively related to either DN4+ or DN4-, PSSP prognosis and treatment should not be solely based on the DN4. Nonetheless, a thorough assessment of neuropathic and nociceptive pain complaints and somatosensory functions should be included in the diagnostic work-up of PSSP.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".