Cultivation and use of medicinal plants and association with reporting of childhood asthma: A case-control study in the Bogotá savanna
Bibliographic record
Abstract
INTRODUCTION: The prevalence of childhood asthma has increased in recent years. The World Health Organization has called for conducting research exploring the role of traditional medicine and medicinal plants in respiratory disease control. OBJECTIVE: To identify the relationship between the prevalence of childhood asthma and traditional care of the respiratory system, including cultivation and use of medicinal plants. METHODS: We conducted an observational, analytic, case-control study that included children 2 to 14 years old who used official health services in eight municipalities near Bogota between 2014 and 2015. Cases were children diagnosed with asthma. We randomly selected the controls among the remaining patients of the same healthcare facilities. We applied an 18-question survey. The Mantel-Haenszel procedure identified significant associations using 95% confidence intervals. RESULTS: We surveyed the caretakers of 97 cases and 279 controls in eight municipalities. Some 23.4% (88/376) and 37.9% (142/375) reported using traditional remedies for fever control and common cold management, respectively. 8.8% (33/376) reported following traditional care during a common cold, 30.4% (114/375) reported growing medicinal plants at home, and 45% (166/369) reported using medicinal plants for health purposes in their household. Multivariate analysis showed that having and using medicinal plants at home is associated with a lower reporting of asthma (odds ratio 0.49; 95% confidence interval: 0.25 to 0.99). CONCLUSIONS: Cultivating and using medicinal plants at home is associated with a lower reporting of childhood asthma. Researchers should consider the therapeutic, environmental, and cultural properties of medicinal plants to prevent respiratory diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".