Assessing Patient Values and Preferences to Inform the 2023 American College of Rheumatology/American College of Chest Physicians Interstitial Lung Disease Guidelines
Bibliographic record
Abstract
OBJECTIVE: Patient engagement is critical to clinical practice guideline (CPG) development. This work presents our approach to ascertaining patients' values and preferences to inform the American College of Rheumatology guidelines for screening, monitoring, and treatment of interstitial lung disease (ILD) in people with systemic autoimmune rheumatic diseases (SARDs). METHODS: We conducted a cross-sectional qualitative study of a purposefully sampled Patient Panel using a modified content analytic approach. The study team reviewed text transcripts from the Patient Panel discussion to identify themes and develop a clustered thematic schema. RESULTS: Twenty-one patients (75% women) participated, with a mean age of 53 years (range 33-73). Patients had one or more SARDs: systemic sclerosis (38%), Sjögren disease (38%), idiopathic inflammatory myopathy (33%), rheumatoid arthritis (24%), and mixed connective tissue disease (10%). We identified 10 themes in 4 thematic clusters: communication, screening and monitoring, treatment goals, and treatment adverse effects. Patients prioritized recognizing ILD symptoms, importance of ILD screening and close monitoring, goals of survival and improving quality of life, and willingness to accept treatment risks provided that there is close communication with providers. Patient representatives shared patients' priorities and insight at the Voting Panel meeting, influencing multiple guideline recommendations. CONCLUSION: Patient engagement fosters a holistic approach to CPG development, leading to recommendations aiming for the best clinical outcomes while prioritizing outcomes important for patients. The patient-identified themes played a critical role in ILD guideline development and provide core elements for shared decision-making as clinicians make management and therapeutic decisions with patients with SARD-associated ILD.
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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 | 0.049 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".