Prospective Study on the Effects of Marijuana Smoking on Lipase and Amylase Activities among Adult Nigerians
Bibliographic record
Abstract
Marijuana consumption is prevalent among young adults in Nigeria, and its effects on various physiological systems are of growing concern. This study investigates the impact of marijuana use on digestive enzymes, specifically serum amylase and lipase, to understand its potential implications on digestive health. This cross-sectional study was conducted in 120 participants comprising of 60 marijuana smokers and 60 non-smokers. Sociodemographic and lifestyle data were collected through structured questionnaires. Serum amylase and lipase activity levels were measured by spectrophotometric method and compared between the two groups. Statistical analysis was performed to assess differences and correlations. The study revealed that marijuana smokers had significantly higher serum amylase and lipase activities compared to non-smokers. Lipase activity correlated positively (r=0.425, p=0.019) with duration of marijuana use. Amylase activity was higher among males than females (r=-0.40, p=0.028). No significant correlation was found between the quantity of marijuana consumed and enzyme activities. Chronic marijuana consumption was associated with increased serum amylase and lipase activities, indicating potential alterations in pancreatic function. These findings suggest that marijuana use may have adverse effects on digestive health, warranting further investigation into the long-term implications. It is recommended that healthcare providers monitor digestive enzyme activities in marijuana users to detect early signs of pancreatic dysfunction.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".