Pain prevalence, phenotypes & management of patients undergoing neurological rehabilitation
Bibliographic record
Abstract
Background Pain is a common and persistent problem in patients with neurological disorders. Despite high prevalence and negative consequences for quality of life and the rehabilitation process, studies have shown that pain frequently remains outside the clinical focus in neurological rehabilitation. Prerequisites for effective pain treatment include accurate assessment and differentiation of the underlying pain mechanism and systematic documentation. This evidence-based approach to pain management is not adequately implemented in this patient group. Objective: To evaluate pain prevalence and phenotypes, to describe interprofessional pain management and patient satisfaction in patients in a Swiss neurorehabilitation clinic. Methods Design: Cross-sectional observational study. Inpatients with diagnosed neurological disorder,>18. Patients with Montreal Cognitive Assessment (MoCA)>19 completed a supported self-report questionnaire, assessing pain prevalence, phenotypes (nociceptive, nociplastic and neuropathic) and management. Patients with MoCA<19 were assessed with the Zurich Observational Pain Assessment (ZOPA). Healthcare professionals completed a questionnaire on pain management. Primary outcome: Pain prevalence. Secondary outcomes: Prevalence of main and additional pain phenotypes, pain severity, association with neurological disorder, patient satisfaction, evaluation of interprofessional pain management and type of prescribed pain medication. Results A total of 79 inpatients were included (57 with questionnaire, 22 with ZOPA). Overall pain prevalence was 58.2% (95%-CI 47.2%–68.5%). Patients completing questionnaire had multiple pain phenotypes (38.5% all 3; 61.5% 2), nociceptive (61.5%) and nociplastic pain (30.8%) were most frequent. Mean pain intensity overall was 3.1 (95%-CI 2.2–4, on Numeric Rating Scale 0–10). 89.5% of healthcare professionals reported assessing pain, primarily severity not pain mechanisms or phenotypes. Standardized, interprofessional procedures were considered to be lacking. Interprofessional pain management was rated “very good” or “good” by 64.9% of patients. Conclusion High pain prevalence and complex mixed phenotype patterns were measured. Standardised interprofessional management including differential pain diagnosis, treatment plans and documentation require improvement. Valid practical tools for phenotype differentiation and implementation strategies for interprofessional pain management require further research.
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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) | 0.000 | 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 teacher head, 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".