Traumatic Brain injury and Cannabis Use : a Primer For Clinicians
Bibliographic record
Abstract
Our clinical experience at a specialized brain injury clinic suggests that numerous patients with traumatic brain injury (TBI) are using cannabis to alleviate their symptoms. While this patient population often inquires about the evidence of using cannabis post head injury for the neurosensory, neurocognitive and neuropsychiatric sequelae, most health professionals have little to no knowledge of this evidence. Given the recent legalization of recreational cannabis in Canada, questions and guidance related to cannabis use following a traumatic brain injury are likely to become more common. This article reviews the evidence for cannabis use in psychiatric disorders with or without traumatic brain injury. Overall, we found that the evidence for the use of cannabis amongst TBI patients is sparse, and that patients tend to have little knowledge of the proven benefits and diverse effects of cannabis use. We feel this paper can serve as a stepping stone for future studies that explore the impact of cannabis use in a TBI population and can guide clinicians in advising their patients.
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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.008 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".