Atopic Dermatitis in Latin America: A Roadmap to Address Data Collection, Knowledge Gaps, and Challenges
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) is a systemic, multifactorial disease that causes significant morbidity and health care burden in Latin America (LA). Data on AD are scarce in LA. Lack of disease registries and non-standardized study methodologies, coupled with region-specific genetic, immunological, and environmental factors, hamper data collection. A panel of LA experts in AD was given a series of relevant questions to address before a conference. Each narrative was discussed and edited through numerous rounds of deliberation until achieving consensus. Identified knowledge gaps in AD research were updated prevalence, adult-disease epidemiology, local phenotypes and endotypes, severe-disease prevalence, specialist distribution, and AD public health policy. Underlying reasons for these gaps include limited funding for AD research, from epidemiology and public policy to clinical and translational studies. Regional heterogeneity requires that complex interactions between race, ethnicity, and environmental factors be further studied. Informed awareness, education, and decision making should be encouraged.
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 | 0.203 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".