Caregiver views on virtual management of food allergy: a mixed-methods study
Bibliographic record
Abstract
1 Conflicts of interestESC has received research support from DBV Technologies; has been a member of advisory boards for Pfizer, Pediapharm, Leo Pharma, Kaleo, DBV, AllerGenis, Sanofi Genzyme, Bausch Health, Avir Pharma; is a member of the healthcare advisory board for Food Allergy Canada; was an expert panel and coordinating committee member of the National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Guidelines for Peanut Allergy Prevention; and was co-lead of the CSACI oral immunotherapy guidelines.SJ has been on speaker’s bureaus for Aralez, Novartis, Astra Zeneca, and Sanofi, and on the advisory board for Sanofi.MH has provided speaker services for Pfizer, Pediapharm, and has been part of an advisory board for ALK and provides privately funded OIT.VC has been a participant on advisory boards for Sanofi Genzyme, Bausch Health, and ALK, speaker services for Aralez Pharmaceuticals and CSL Behring.DM has provided consultation and speaker services for Pfizer, Aimmune, Kaleo, Merck, Covis and Pediapharm, and has been part of an advisory board for Pfizer and Bausch Health. He sits on the editorial board for the Journal of Food Allergy.EA Section Head of Anaphylaxis/Food Allergy for the Canadian Society of Allergy and Clinical Immunology; sits on steering committee for Canada’s National Food Allergy Action Plan; moderator/speaker fees from Novartis, GSK, Sanofi, AstraZeneca.LS NoneTW speaking engagements for Pfizer and Stallergenes Greer, Advisory Board member for ALK and Leo PharmaJP is the Section Head of Allied Health for the Canadian Society of Allergy and Clinical Immunology; and sits on the steering committee for Canada’s National Food Allergy Action Plan
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".