Exploring medical cannabis use in individuals with a traumatic brain injury
Bibliographic record
Abstract
Aim: Traumatic brain injury (TBI) is a common neurological condition, which can present with a wide range of neuropsychological symptoms. Treating this broad spectrum of symptoms represents a significant medical challenge. In part because of this, there is growing interest in the use of medical cannabis to treat the sequelae of TBI, as medical cannabis has been used to treat multiple associated conditions, such as pain. However, medical cannabis represents a heterogeneous collection of therapies, and relatively little is known about their effectiveness in treating TBI symptoms. The aim of the present study was therefore to assess medical cannabis use in patients with TBI. Methods: In the present study, a retrospective chart review was conducted of patterns of cannabis use and TBI symptoms in individuals who used medical cannabis to treat TBI-related symptoms. All subjects were recruited from a medical cannabis clinic, where cannabis was authorized by physicians, using licensed cannabis products. A total of 53 subjects provided written consent to have their charts reviewed. Results: Neuropsychiatric conditions, including depression, pain, and anxiety were frequent in this group. The most common forms of medical cannabis consumption at intake included smoking, vaping, and oral ingestion. Patients used a combination of high tetrahydrocannabinol (THC)/low cannabidiol (CBD) and low THC/high CBD products, typically 1–3 times per day. Medical cannabis appeared to be relatively well-tolerated in subjects, with few serious side effects. At follow-up, subjects self-reported improvements in TBI symptoms, although these were not statistically significant when assessed using validated questionnaires. Conclusions: Overall findings indicate modest potential benefits of medical cannabis for TBI, but further research will be required to validate these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".