Nasal mucosal reactivity assessment via a double-blind placebo-controlled food challenge with cow’s milk allergens
Bibliographic record
Abstract
BACKGROUND: Allergies, including food allergies, are a considerable clinical and public-health problem. The introduced preventive measures and differential diagnostics, including oral food challenges, are the gold standard for determining further treatment planning. CASE PRESENTATION: We present a case of an 18-year-old girl with a cow's milk allergy who underwent an oral food challenge (double blind oral food challenge). Such a challenge may be confounded by inducing a response from other systems and organs, which provides theoretical grounds for the use of other methods of assessing the body's response to food allergens (response demonstrated by the upper respiratory tract). Based on this idea, in order to assess the degree of mucosal response, we used optical rhinometry as an objective method for nasal patency evaluation, as well as identification of tryptase level in nasal lavage fluid and exfoliative cytology of nasal mucosa. The results of these tests confirmed positive reaction of the nasal mucosa in the course of the oral allergen challenge. CONCLUSIONS: The observed increase in the nasal mucosal reactivity that accompanies oral food challenges may suggest a potential for using food allergens in nasal allergen provocation testing in order to diagnose food allergies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".