The role of Sativex in robotic rehabilitation in individuals with multiple sclerosis
Bibliographic record
Abstract
INTRODUCTION: Currently, none of the available multiple sclerosis (MS) disease-modifying medications has been shown to stop or reverse gait disability. Recently, the nabiximols has been tested for the treatment of spasticity and walking impairment in MS. Nabiximols (trade name Sativex) is an oromucosal spray formulation containing 1:1 fixed ratio of delta-9-tetrahydrocannabinol and cannabidiol derived from cloned Cannabis sativa L. plant. METHOD AND ANALYSIS: A single-center, prospective, parallel design, single-blind trial will be conducted at the IRCCS Neurolesi "Bonino-Pulejo" (Italy) involving MS patients affected by spasticity and undergoing a Robotic Rehabilitation training. The aim of the study is to clarify the role of Sativex coupled to a robotic neurehabilitation training in MS patients in improving motor outcomes, by means of clinical, kinematic, and neurophysiological measures. Patients will be randomly divided in 2 groups: one taking only an oral antispastic drug and the other with Sativex in add-on. After 1 month, we will evaluate the response to Sativex (responder patients' amelioration >20% at MRS score) enrolling into the study the first 20 patients with a good response to Sativex, whereas other 20 no-responder individuals will continue their antispastic drug. All the 40 subjects, were divided into 2 groups (A: Sativex + Lokomat Training, and B: other antispastic+Lokomat Training), will perform a neurorobotic-assisted gait training (each session will last at least 45 minutes, 3 times per week, for a total of 20 sessions). All the patients will undergo a complete physical and neurological examination at baseline, at the end of the robotic training (T1), and 30 days after the end of the neurorehabilitation training (T2).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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".