A <i>post hoc</i> evaluation of the shift in spasticity category in individuals with multiple sclerosis-related spasticity treated with nabiximols
Bibliographic record
Abstract
Background: Over 80% of individuals with multiple sclerosis (MS) experience MS-associated spasticity (MSS). In many European countries, after failure of first-line treatments, moderate or severe MSS can be treated with nabiximols, a cannabis-based add-on treatment. Objective: This post hoc analysis assessed the shift of participants treated with nabiximols from higher (severe or moderate) to lower (moderate or mild/none) spasticity. Methods: Previously published data from two randomised controlled trials (RCTs), GWSP0604 (NCT00681538) and SAVANT (EudraCT2015-004451-40), and one large real-world study (consistent with EU label), all enriched for responders, were re-analysed. Spasticity severity, measured using the 0–10 numerical rating scale (spasticity NRS), was categorised as none/mild (score <4), moderate (score ⩾4–7), or severe (score ⩾7). Results: In the two RCTs, the shift of participants with severe MSS into a lower category was significantly greater at week 12 for those receiving nabiximols versus placebo [GWSP0604: OR (95% CI), 4.4 (1.4, 14.2), p = 0.0125; SAVANT: 5.2 (1.2, 22.3), p = 0.0267]. In all three studies, over 80% of assessed patients with severe spasticity at baseline reported a shift into a lower category of spasticity after 12 weeks. Conclusions: A meaningful proportion of MSS patients treated with nabiximols shifted to a lower category of spasticity severity, typically maintained to the end of the 12-week study period.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.020 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".