Impact of <i>Cannabis Sativa</i> (Marijuana) Smoke on Alveolar Bone Loss: A Histometric Study in Rats
Bibliographic record
Abstract
BACKGROUND: Cannabis sativa (marijuana) can interfere with bone physiopathology because of its effect on osteoblast and osteoclast activity. However, its impact on periodontal tissues is still controversial. The present study evaluates whether marijuana smoke affects bone loss (BL) on ligature-induced periodontitis in rats. METHODS: Thirty male Wistar rats were used in the study. A ligature was placed around one of the mandible first molars (ligated teeth) of each animal, and they were then randomly assigned to one of two groups: control (n = 15) or marijuana smoke inhalation ([MSI] for 8 minutes per day; n = 15). Urine samples were obtained to detect the presence of tetrahydrocannabinol. After 30 days, the animals were sacrificed and decalcified sections of the furcation area were obtained and evaluated according to the following histometric parameters: bone area (BA), bone density (BD), and BL. RESULTS: Tetrahydrocannabinol was positive in urine samples only for the rats of the MSI group. Non-significant differences were observed for unligated teeth from both groups regarding BL, BA, and BD (P >0.05). However, intragroup analysis showed that all ligated teeth presented BL and a lower BA and BD compared to unligated teeth (P <0.05). The intergroup evaluation of the ligated teeth showed that the MSI group presented higher BL and lower BD (P <0.05) compared to ligated teeth from the control group. CONCLUSION: Considering the limitations of this animal study, cannabis smoke may impact alveolar bone by increasing BL resulting from ligature-induced periodontitis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".