Pain in Individuals With Multiple Sclerosis, Knee Prosthesis, and Post-herpetic Neuralgia
Bibliographic record
Abstract
INTRODUCTION: Pain is a common symptom in patients with multiple sclerosis (MS) and it is thought to be the result of a mixture of neuropathic and nociceptive pain. Different elements of pain need to be recognized and treated differently, but a clinical tool to classify these components still remains to be defined. AIM: The aim of our study was to evaluate subjective feeling of pain in people with MS, including pain quality description and pain impact in daily functioning. We also investigated which descriptors are related to nociceptive pain and which to neuropathic pain. Finally, we explored if there are differences between the descriptors spontaneously used by individuals with MS and the ones included in the McGill Pain Questionnaire (MGPQ). METHODS: We used focus group (FG) of discussion to collect participants' opinion about their pain. We organized 2 FGs for persons with MS. We also gathered 2 FGs of individuals who had a recent knee arthroplasty, suffering a pure nociceptive pain, and 2 FGs of individuals with post-herpetic neuralgia, suffering a pure neuropathic pain, to compare their experience with the one of the people with MS. RESULTS: Original spontaneous descriptors emerged in all the groups. People with MS in particular used various symbolic descriptors to express their pain's quality and underlined the high impact of pain on their lives. The use of specific descriptors for neuropathic and nociceptive pain in the different groups did not appear easily definable. Finally, pain descriptors used during FG appeared to be different than the ones included in the MGPQ. CONCLUSIONS: Original spontaneous descriptors, possibly pathology-specific, emerged in all groups not included in the MGPQ and pointed out the need to use assessment tools based on people experience.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".