Asthma in Latin America
Bibliographic record
Abstract
Consistent with the diversity of Latin America, there is profound variability in asthma burden among and within countries in this region. Regional variation in asthma prevalence is likely multifactorial and due to genetics, perinatal exposures, diet, obesity, tobacco use, indoor and outdoor pollutants, psychosocial stress and microbial or parasitic infections. Similarly, non-uniform progress in asthma management leads to regional variability in disease morbidity. Future studies of distinct asthma phenotypes should follow-up well-characterised Latin American subgroups and examine risk factors that are unique or common in Latin America (eg, stress and violence, parasitic infections and use of biomass fuels for cooking). Because most Latin American countries share the same barriers to asthma management, concerted and multifaceted public health and research efforts are needed, including approaches to curtail tobacco use, campaigns to improve asthma treatment, broadening access to care and clinical trials of non-pharmacological interventions (eg, replacing biomass fuels with gas or electric stoves).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".