Patients' values and preferences for health states in allergic rhinitis—An artificial intelligence supported systematic review
Bibliographic record
Abstract
BACKGROUND: Allergic rhinitis (AR) impacts patients' physical and emotional well-being. Assessing patients' values and preferences (V&P) related to AR is an essential part of patient-centered care and of the guideline development process. We aimed to systematically summarize the information about patients' V&P on AR and its symptoms and impact on daily life. METHODS: We conducted systematic review in a MEDLINE, Embase, PsychInfo, and CINAHL databases. We included studies which quantitatively assessed patients' V&P for specific outcomes in AR by assessing utilities, applying discrete choice approaches, or rating and ranking outcomes. We grouped outcomes as AR symptoms, functional status, and care-related patient experience. Study selection and data extraction were supported by the Laser AI tool. We rated the certainty of evidence (CoE) using the GRADE approach. RESULTS: Thirty-six studies (41 records) were included: nine utility studies, seven direct-choice studies and 21 studies of rating or ranking outcomes. Utilities were lower with increased AR severity and with the concomitant presence of asthma, but not with whether AR was seasonal or perennial (CoE = low-high). Patients rated AR symptom-related outcomes as more important than those related to care-related patient experience and functional status (CoE = very low-moderate). Nasal symptoms (mainly nasal congestion) followed by breathing disorders, general and ocular symptoms were rated as the symptoms with the highest impact. CONCLUSIONS: This systematic review provides a comprehensive overview of V&P of patients with AR. Patients generally considered nasal symptoms as the most important. Future studies with standardized methods are needed to provide more information on V&P in AR.
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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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".