Multiple sclerosis-induced neuropathic pain
Bibliographic record
Abstract
Neuropathic pain (NPP) is a chronic syndrome suffered by patients with multiple sclerosis (MS), for which there is no cure. Underlying cellular mechanisms involved in its pathogenesis are multifaceted, resulting in significant challenges in its management. In addition to its complex pathophysiology, the clinical management of MS-induced NPP is further complicated by the lack of clinical therapeutics trials specific to this population. The primary aim of the work underlying this thesis was to contribute to the evidence-based management of individuals with MS-induced NPP through the completion of two clinical therapeutics trials in this population. A secondary aim was to describe pain variability in this patient population through the development and validation of a pain variability algorithm tool. Resulting from this work, we demonstrated that nabilone – a synthetic oral cannabinoid – represents an effective, well-tolerated and novel treatment for MS-induced NPP. Additionally, we show that the SSRI paroxetine was poorly tolerated in our patient population, with a correspondingly high attrition rate. As a result, we were unable to determine any treatment effect in this trial due to insufficient recruitment due to drop-out. Lastly, we were able to define and describe pain instability in this cohort, noting that approximately 30% of individuals with MS-induced NPP experiencing highly variable daily pain. The results of these projects provide novel information for this patient population. Patients currently living with the daily burden of MS-induced NPP would benefit from additional trials ensuing from this, and other, research in order to initiate a momentum for much-needed clinical research in this complicated patient cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".