Novel Approach Leveraging Social Media Indicates Complementary and Alternative Medicine Use Highly Prevalent and Is Sometimes Associated with Serious Adverse Events in Patients With Autoimmune Hepatitis
Bibliographic record
Abstract
Introduction: The use of CAM by patients with chronic liver disease has been explored, but the use of CAM products by patients with autoimmune hepatitis (AIH) is unknown. We sought to investigate the frequency and characteristics of CAM use by patients with AIH by interrogating two large and interactive AIH-specific social media groups. Methods: An invitation to complete a CAM-specific questionnaire was posted every other day to wellestablished autoimmune hepatitis Facebook communities (AHRN and AIHA) during a 10 day study period. Combined membership of these two closed social media groups was 2600 individuals with AIH. The survey contained 22 questions and collected patient demographics, disease characteristics, and CAM use. Age ≥ 18 years and an AIH diagnosis by an MD were the eligibility criteria. Results: A total of 401 individuals with AIH completed the questionnaire. Respondents were 91% female with average age at survey completion of 49.3 yrs and average age at AIH diagnosis of 42.9 yrs. 36% reported early fibrosis and 37% advanced fibrosis on liver biopsy nearest to their diagnosis. 90% were currently treated for AIH including 41% on prednisone, 11% budesonide, 69% thiopurines, and 15% purine analogues. 246 (61%) respondents reported any CAM during their lifetime; while 220 (55%) reported CAM use prior to and 193 (48%) after their AIH diagnosis. The most common reasons for CAM prior to their diagnosis was overall health improvement (77%), better sleep (27%), and weight loss (23%). The most common reasons for CAM use after AIH diagnosis was overall health improvement (56%), immune support (32%), and joint pain (29%). Eighty five (44%) respondents who used CAM after their diagnosis reported using CAM to treat AIH related symptoms, most commonly joint pain (94%), fatigue (86%), and sleep disturbances (78%). The CAM use after diagnosis was associated with younger age at diagnosis (41.5 yrs vs. 44 yrs, p=0.04) and higher education (college or more) (84% vs 16%) (p=0.001), but not gender, current age, liver fibrosis or medications. Ten (4%) CAM users reported severe adverse events (SAEs), 10 (4%) emergency room visits secondary to CAM and 3 (1%) requiring hospitalization. Headaches, worsening fatigue/joint pain, and gastrointestinal symptoms were the most common SAEs. Conclusion: There is high prevalence of CAM use among patients with AIH and their use is associated with younger age and higher education. Nearly 5% of CAM users reported SAEs with 1% requiring hospitalization.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".