Bibliographic record
Abstract
In a 10-minute consultation, Aziz Sheikh and Samantha Walker outline how to approach a patient with a suspected food allergy (p 1337). They point out that 20% of the population believe that they have a food allergy, but, with only 1% responding to a controlled food challenge, the public has vast misconceptions about what food allergy is. As ever, the internet is a great source of well meaning information, only some of which is useful. People tend not to understand the difference between a true allergy and intolerance; given the potential market for self testing kits, some commercial websites do nothing to dispel such misunderstanding. Clear information aimed at both professionals and consumers can be found at the site of the American Academy of Allergy, Asthma, and Immunology (www.aaaai.org/default.stm). As well as offering a good explanation of the difference between allergy and intolerance, the site allows patients and consumers to search allergic conditions and directs them to relevant resources. For professionals there is information about disease management and careers in allergy and immunology. Also from the United States comes the New York based Food Allergy Initiative (www.foodallergyinitiative.org), a non-profit making organisation aimed at helping improve the lives of those living with a life threatening allergy. As it soberingly highlights, one bite containing a minuscule amount of the wrong food can be fatal. It pushes for proper labelling of foods that people can trust and for restaurants to treat the problem with the seriousness it deserves. The Food Allergy and Anaphylaxis Network (www.foodallergy.org) has a similar remit. Its website has extensive resources that go beyond education, such as recipes and a tip of the day. The Food Allergy and Anaphylaxis Alliance (www.foodallergyalliance.org/foo.html) outlines facts about food allergy and related issues from the perspectives of Australia, New Zealand, Canada, the Netherlands, the United Kingdom, and the United States. It gives the top three concerns for each country (for example, food labelling is one of those listed for Australia) and practical advice such as the phone numbers for emergency services.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.259 | 0.193 |
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".