Practical challenges in oral immunotherapy resolved through patient-centered care
Bibliographic record
Abstract
Oral immunotherapy (OIT) is now widely recognized as a valid option for the management of IgE-mediated food allergies. However, in real-life practice, OIT can lead to a variety of unique situations where the best course of action is undetermined. In patient-centered care, individual patient preferences, needs and values, should guide all clinical decisions. This can be achieved by using shared-decision making and treatment customization to navigate areas of uncertainty in a way that is responsive to patient's needs and preferences. However, in the context of OIT, lack of awareness of potential protocol adaptability or alternatives can become a barrier to treatment personalization. The purpose of this article is to review the theoretical bases of patient-centered care and shared decision-making and their practical implication for the patient-centered delivery of OIT. Clinical cases highlighting common challenges in real-life OIT practice are presented along with a discussion of potential personalized management options to be considered. While the practice of OIT is bound to evolve as additional scientific and experiential knowledge is gained, it should always remain rooted in the general principles of patient-centered care.
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 | 0.006 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".