Embedding patients' values and preferences in guideline development for allergic diseases: The case study of Allergic Rhinitis and its Impact on Asthma 2024
Bibliographic record
Abstract
Recommendations for or against the use of interventions need to consider both desirable and undesirable effects as well as patients' values and preferences (V&P). In the decision-making context, patients' V&P represent the relative importance people place on the outcomes resulting from a decision. Therefore, the balance between desirable and undesirable effects from an intervention should depend not only on the difference between benefits and harms but also on the value that patients place on them. V&P are therefore one of the criteria to be considered when formulating guideline recommendations in the Evidence-to-Decision framework developed by the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) Working Group. Patients' V&P may be quantified through utilities, which can be elicited using direct methods (e.g., standard gamble or time trade-off) or indirect methods (using validated instruments to measure health-related quality of life, such as EQ-5D). The GRADE approach recommends conducting systematic reviews to summarise all the available evidence and assess the degree of certainty on V&P. In this article, we discuss the importance of considering patients' V&P and provide examples of how they are considered in the 2024 person-centred Allergic Rhinitis and its Impact on Asthma (ARIA) guidelines.
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 | 0.087 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".