Microbial dysbiosis in cannabis smoking is associated with worse respiratory symptoms
Bibliographic record
Abstract
Introduction: ~220 million people use cannabis annually. While smoking cannabis worsens respiratory symptoms and alters immune response, the impacts of smoking cannabis on lung health are unclear. Objective: Explore potential outcomes of cannabis smoke on the lung microbiome and respiratory symptoms. Methods: 26 cannabis smoking (CS) and 24 non-smoking (NS) participants completed the St. Georges Respiratory Questionnaire (SGRQ) and underwent bronchoalveolar lavage (BAL) collection for 16S rRNA sequencing. Alpha and beta diversity was measured using Shannon diversity index and Weighted UniFrac with PERMANOVA, adjusted for age and sex. Relative abundance (RA) of 10 most abundant genera were compared between groups. Spearman’s correlation was tested between diversity/RA measures and SGRQ scores. Results: SGRQ scores were worse in CS (p=0.008). Alpha diversity was similar, but in CS lower Shannon index was correlated with worse SGRQ scores (Fig 1A). Beta diversity was different between CS and NS (p=0.026, Fig 1B). In CS, RA was higher in Prevotella (p=0.046) and Veillonella (p=0.009) and was lower in Neisseria (p=0.035) compared to NS. In CA, Streptococcus RA increased with joint-years (p=0.011, Fig 1C) and with worse SGRQ symptom scores (p=0.038, Fig 1D). Conclusions: Cannabis smoke is associated with changes in the airway microbiome, which may contribute to worse respiratory symptoms. erj;66/suppl_69/PA5236/F1 F1 F1
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".