Bibliographic record
Abstract
Peanut allergy is common and can be a cause of severe, life-threatening reactions. It is rarely outgrown like other food allergies such as egg and milk. Measures aiming to reduce its prevalence via maternal avoidance during pregnancy and lactation, or delayed introduction into the diet, have failed to show any benefit. Peanut allergy has a significant effect on the quality of life of sufferers and their families due to dietary and social restrictions, but mainly stemming from fear of accidental peanut ingestion. The current management consists of strict avoidance, education and provision of emergency medication. Families find avoidance challenging as peanut is hidden in various food products. Despite the fact that food labelling has improved, with a legal obligation to declare certain food allergens (including nuts) in prepacked products, it still causes confusion and does not extend to cross-contamination. In an effort to address issues of safety at school, a lot of work has been undertaken to better care for peanut-allergic children in that environment. This includes training of school staff on how to recognise and treat allergic reactions promptly. Recent developments in the management of peanut allergy, such as immunotherapy, have shown some promise as an active form of treatment, but larger studies are required to further investigate safety and efficacy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.002 |
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".