Evaluation of Breath and Plasma Tetrahydrocannabinol Concentration Trends Postcannabis Exposure in Medical Cannabis Patients
Bibliographic record
Abstract
The legalization of cannabis in Canada brings novel challenges across various fronts, such as policy development, law enforcement, and public health and safety. It is imperative to improve our understanding of the mechanisms and trends surrounding cannabis use to develop efficacious methods of tackling these challenges. Materials and Methods: Patients' breath collection was achieved using the ExaBreath device from SensAbues. THC measurements in plasma and breath samples were processed and analyzed using LC-MS/MS. Discussion: We conducted a pragmatic clinical trial on 23 medical cannabis patients, wherein we collected breath and plasma samples intermittently for 4 hours after cannabis consumption. The research participants consumed between 1 and 2 g of cannabis by either vaping, cannabis cigarette, or concentrated wax (dabs) for 10 min. We used standardized laboratory analytical techniques using liquid chromatography–tandem mass spectrometry to analyze both the breath and plasma sample. To analyze the data and find patterns, we developed models using artificial neural network analysis. Conclusion: Our findings show that tetrahydrocannabinol (THC) breath concentrations peaked in 0.5 hours and reached baseline levels after 2 hours in all the patients. We found an inverse correlation between individuals' body mass index and their peak breath concentrations, and an inverse relationship between age and peak breath concentrations. Male participants had higher peak breath and plasma concentrations than female participants. Our research provides new insight into the correlations between breath and plasma THC concentrations in medical cannabis patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".