Tree nut introduction in a peanut‐allergic child: To eat, to screen, or to avoid?
Bibliographic record
Abstract
There is no defined standard of care around tree nut introduction in a peanut-allergic child, and the role of screening prior to tree nut introduction is controversial. There is some evidence that peanut-allergic children are at increased risk of tree nut allergy, with approximately 23-68% of children with co-existent peanut/tree nut allergy. In some studies, it has been shown that tree nut allergy in children has the potential to be a severe allergy. However, this appears to be age-specific as infant anaphylaxis in general tends to be milder, and there has been no fatality reported on the first ingestion of an allergen in infancy. Familial hesitancy has been identified as a possible condition for undertaking screening tests prior to allergen introduction. Indeed, there has been limited evidence that caregiver hesitancy may exist in peanut-allergic families with tree nut introduction. However, pre-emptive screening has the potential to overdiagnose tree nut allergy and delay introduction (which could paradoxically increase risk). As a result, the decision is best made in the context of shared decision-making and patient preference-sensitive care.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".