An overview of current clinical practice and international oral immunotherapy guidelines: A focus on Spanish, European, and Canadian guidelines
Bibliographic record
Abstract
Oral immunotherapy (OIT) is a recent and evolving therapeutic option for the treatment of immunoglobulin E (IgE) mediated food allergies. Clinical practice guidelines are starting to emerge to establish the parameters of this new clinical offer. A comparative analysis reveals several areas of consensus, such as the need for an accurate diagnosis with immunoglobulin E testing and, if necessary, open food challenge before initiating therapy; a list of specific contraindications; the importance of performing OIT in an adequate setting with appropriate level of expertise; the possibility to use grocery products to perform OIT; and the need to adapt protocols to patient needs. Certain discrepancies among the guidelines also underscore various areas of uncertainty, which makes it important that decisions to pursue the treatment be reached by using a shared decision-making approach that involves patients and caregivers. Gaps of knowledge remain with regard to treatment of adolescents and adults, and optimal performance measures in practice. These guidelines are expected to evolve in the coming years as new scientific and experiential knowledge is gained.
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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".