Bibliographic record
Abstract
Legumes other than peanut are an important source of protein and consist of a wide variety of species, such as soy, peas, chickpeas, lentils, and lupin. Due to their health benefits and the rising popularity of veganism, legume consumption has increased. Legume allergy, cross-sensitization, and cross-reactivity between different species have been reported in the literature and are increasingly recognized. Unlike peanut, oral immunotherapy (OIT) for nonpeanut legumes has not been well studied and published protocols are lacking. Future studies are needed to provide real-world data on the safety and effectiveness of nonpeanut legume OIT, and whether desensitization to one legume leads to desensitization to other legumes in patients with multiple legume allergy. Nevertheless, due to the abundance of clinical trial and real-world data for peanut OIT, it is reasonable to use protocols that substitute peanut protein with other legume protein when desensitizing individuals with nonpeanut legume allergy. Clinicians who are starting to offer legume OIT in their practices may consider starting with preschoolers, an age group for whom real-world data has shown the greatest safety and effectiveness.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.048 | 0.017 |
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".