Bibliographic record
Abstract
The prevalence of obesity has increased worldwide, and weight gain has been shown to influence the development and clinical expression of various conditions including asthma. The relationships between atopy and obesity remain uncertain, both in adults and in children. Although there are physiopathologic mechanisms which could explain how obesity could influence the immune system and promote the process of sensitization, evidences in favour of a possible role of obesity on the development of atopy have been inconsistent. Furthermore, the bulk of evidence suggests that atopy does not mediate the relationship between obesity and asthma, although in some populations, particularly in children and women, such association has been reported. Such lack of relationship has also been found with rhinoconjunctivitis although it has been observed for atopic dermatitis. Several factors may explain these variable results, including populational or environmental characteristics, socioeconomic status, confounding factors, in addition to sample size, and methodology of the performed studies. The possibility that obesity influences atopy through its effects on sex hormones is suggested by a more frequent link between atopy and obesity in women, particularly postpuberal. Further research should be conducted on the influence of weight gain on atopy and atopic 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".