Cannabis Use, Premorbid History and Symptom Severity Following a Concussion : a Cross Sectional Study in a Canadian Tertiary Care Clinic
Bibliographic record
Abstract
Background: Close to 14% of Canadians aged 15 years and older reported some use of cannabis products for medical or non-medical use. Non-prescriptive drug use of cannabis has been reported among patients with a concussion. The recent legalization of cannabis in Canada is leading health practitioners to investigate patient-reported use of cannabis on concussion care practices. Objective: We hypothesize that premorbid history and symptom severity may contribute to a patientu2019s use of cannabis following a concussion. We sought to compare the relationships between premorbid characteristics and symptom severity scores with cannabis use in an adult population of patients attending a tertiary care clinic (TCC) for persistent post-concussion symptoms. Methods: Patients in the study population visited the TCC for post-concussion care from July 2013 to December 2017 and completed the survey at the time of their first visit. Cannabis use was defined as patient-reported use in the past 12 months before the concussion and/or current use. Premorbid characteristics included history of learning disability, ADD/ADHA, anxiety and depression. Symptom severity was measured using the Rivermead Post Concussion Questionnaire (RPQ).Chi Square statistics were used to test for significant relationships.Results: The prevalence of cannabis use among the TTC study population was 10.9% (179/1643). At this TCC, patients with or without learning disability had a rate of cannabis use of 28.6% vs.10.2% (p<.05). Similarly patients with or without a history of ADD/ADHD had a rate of cannabis use of 34.4% vs. 9.9% (p<.05). In patients with or without a history of depression had a rate of cannabis use of 15.8% vs. 10.0% (p<.05). While patients with or without a history of anxiety had a rate of cannabis use of 15.6% vs. 10.2% (p<.05). Whereas, in contrast, patients with or without high RPQ symptom scores had a rate of cannabis use of 10.7% vs.15.9% (p<.05). Discussion: In our TCC study population we determined the rate of cannabis use in several premorbid conditions. In the treatment of concussion it may be helpful for clinicians to know along with their premorbid history, if the patient is taking cannabis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".