Decisions With Patients, Not for Patients: Shared Decision-Making in Allergy and Immunology
Bibliographic record
Abstract
Shared decision-making (SDM) is an increasingly implemented patient-centered approach to navigating patient preferences regarding diagnostic and treatment options and supported decision-making. This therapeutic approach prioritizes the patient's perspectives, considering current medical evidence to provide a balanced approach to clinical scenarios. In light of numerous recent guideline recommendations that are conditional in nature and are clinical scenarios defined by preference-sensitive care options, there is a tremendous opportunity for SDM and validated decision aids. Despite the expansion of the literature on SDM, formal acceptance among clinicians remains inconsistent. Surprisingly, a significant disparity exists between clinicians' self-reported adherence to SDM principles and patients' perceptions of its implementation during clinical encounters. This discrepancy underscores a fundamental issue in the delivery of health care, where clinicians may overestimate their integration of SDM, while patients' experiences suggest otherwise. This review critically examines the factors contributing to this inconsistency, including barriers within the health care system, clinician attitudes and behaviors, and patient expectations and preferences. By elucidating these factors in the fields of food allergy, asthma, eosinophilic esophagitis, and other allergic diseases, this review aims to provide insights into bridging the gap between clinician perception and patient experience in SDM. Addressing this discordance is crucial for advancing patient-centered care and ensuring that SDM is not merely a theoretical concept but a tangible reality in the.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".