Living With Chronic Rhinosinusitis: Insights From an Arts‐Based Study
Bibliographic record
Abstract
Background: Chronic rhinosinusitis (CRS) significantly reduces quality of life (QoL), but data regarding the extent of its impact is sparse. Questionnaire-based assessments of QoL may neglect aspects of complex patient experiences. Recent studies on asthma patients and other chronic conditions have used self-expression through artwork to better depict patients' experiences. This study aims to analyze the experience of living with CRS by exploring common characteristics represented within artworks. Methods: = 16). Disease severity and depression and anxiety were graded using standardized scales. Patient experience was evaluated using drawings and semi-structured interviews. ChatGPT-4, a generative large-language model, was used to interpret interview transcripts according to the Common-Sense Model for Self-Regulation to identify themes. Results: Analysis of artworks through interviews identified six main themes: "chronicity and adaptation," "impacts," "emotional toll," "healthcare navigation and advocacy," "resilience and personal growth," and "complexity and nuance." These reveal in greater detail a multifaceted and contradicting emotional landscape shaped by chronic illness. For patients who scored high on depression and anxiety scales, the emotional toll and impacts were more prominently depicted in interviews. Compared with similar studies conducted in patients with asthma, these results highlight the more prevalent difficulties of navigating the healthcare system for patients with CRS. Conclusion: An arts-based methodology enables in-depth exploration of the impact of CRS on QoL, using large language models, a type of artificial intelligence, to identify common themes amongst individual experiences of CRS patients.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".