Evaluation of Self-reported Patient Experiences: Insights from Digital Patient Communities in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the types of experiences and treatment access challenges of patients with psoriatic arthritis (PsA) using self-reported online narratives. METHODS: English-language patient narratives reported between January 2010 and May 2016 were collected from 31 online sources (general health social networking sites, disease-focused patient forums, treatment reviews, general health forums, mainstream social media sites) for analysis of functional impairment and 40 online sources for assessment of barriers to treatment. Using natural language processing and manual curation, patient-reported experiences were categorized into 6 high-level concepts of functional impairment [social, physical, emotional, cognitive, role activity (SPEC-R), and general] and 6 categories to determine barriers to treatment access (coverage ineligibility, out-of-pocket cost, issues with assistance programs, clinical ineligibility, formulary placement/sequence, doctor guidance). The SPEC-R categorization was also applied to 3 validated PsA patient-reported outcome (PRO) instruments to evaluate their capacity to collect lower-level subconcepts extracted from patient narratives. RESULTS: Of 15,390 narratives collected from 3139 patients with PsA for exploratory analysis, physical concepts were the most common (81.5%), followed by emotional (50.7%), cognitive (20.0%), role activity (8.1%), and social (5.6%) concepts. Cognitive impairments and disease burden on family and parenting were not recorded by PsA PRO instruments. The most commonly cited barriers to treatment were coverage ineligibility (51.6%) and high out-of-pocket expenses (31.7%). CONCLUSION: Patients often discussed physical and emotional implications of PsA in online platforms; some commonly used PRO instruments in PsA may not identify cognitive issues or parenting/family burden. Nearly one-third of patients with PsA reported access barriers to treatment.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".