An Open‐Label Comparison of Nabilone and Gabapentin as Adjuvant Therapy or Monotherapy in the Management of Neuropathic Pain in Patients with Peripheral Neuropathy
Bibliographic record
Abstract
Neuropathic pain (NeP) is prevalent in patients with peripheral neuropathy (PN), regardless of etiology. We sought to compare the efficacy of the cannabinoid nabilone as either monotherapy or adjuvant therapy with a first-line medication for NeP, gabapentin, in a patient population with PN-NeP. Patients diagnosed with PN-NeP were permitted to initiate monotherapy (nabilone or gabapentin) or add one of these two medications (adjuvant therapy) to their existing NeP treatment regimen in a non-randomized open-label nature. Baseline data collected included a primary outcome (visual analog scores [VAS] of pain) and secondary outcomes (quality of life [EuroQol 5 Domains and Short-Form 36] assessments and assessments of sleep [Medical Outcomes Sleep Study Scale {MOSSS}], anxiety and depression [Hospital Anxiety and Depression Scale], and pain [Brief Pain Inventory]). Reassessment and modulation of dosing and/or medications occurred at 3- and 6-month intervals. Medication adverse effects and drug efficacy, as well as questionnaires, were assessed at 6 months. Matched analysis of variance testing was performed to compare 3- and 6-month scores with baseline, as well as to compare therapies at equal time points. Significant improvements in pain VAS were seen in all treatment groups at 6 months. Numerous sleep parameters within MOSSS, Brief Pain Inventory, and Short-Form 36 improved in patients receiving nabilone or gabapentin either as monotherapy or adjuvant treatment. Hospital Anxiety and Depression Scale-A scores were significantly improved in all treatment groups. Sleep adequacy and the sleep problems index within the MOSSS improved in nabilone monotherapy patients in particular. The benefits of monotherapy or adjuvant therapy with nabilone appear comparable to gabapentin for management of NeP. We advocate for head-to-head randomized, double-blind studies for current therapies for NeP in order to determine potential advantages beneficial in this patient population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".