A systematic review of how cannabinoids affect motoneuron output
Bibliographic record
Abstract
Spinal motoneurons contain many ion channels and receptors upon which various cannabinoids are known to act. This scoping review involved the synthesis of evidence from literature published before August 2022 about the effects of cannabinoids on quantifiable measures of motoneuron output. Four databases (MEDLINE, Embase, PsycINFO, and Web of Science CoreCollection) were queried and 4,237 unique articles were retrieved. Twenty-three studies met the inclusion criteria, and the findings from these studies were grouped according to four emergent themes: rhythmic motoneuron output, afferent feedback integration, membrane excitability, and neuromuscular junction transmission. This synthesis of evidence suggests that CB1 agonists can increase the frequency of cyclical patterns of motoneuron output (i.e., fictive locomotion). Furthermore, a majority of the evidence indicates that activating CB1 receptors at motoneuron synapses promotes excitation of motoneurons by enhancing excitatory synaptic transmission and depressing inhibitory synaptic transmission. The collated study results reveal variable effects of cannabinoids on acetylcholine release at the neuromuscular junction, and the influence of cannabinoids in this area requires more work to ensure precision of findings for CB1 agonist and antagonist impact. Altogether, these reports indicate that the endocannabinoid system is integral within the final common pathway and can impact motor output. This review contributes to understanding the effects of endocannabinoids on synaptic integration at the motoneuron and modulation of motor output.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".