Disease Burden of IgA Nephropathy (IgAN): A Linguistic Analysis of Global Social Media Conversations over Time: A Comparison of Results, 2019-2023
Bibliographic record
Abstract
Background: The treatment landscape for Immunoglobulin A Nephropathy (IgAN) has evolved with the approval of several new therapies for patients requiring a comparative evaluation of a prior and updated ethnographic study to understand changes in patient experiences of disease manifestations, disease impact, and treatment of IgAN as described by patients, caregivers, and healthcare providers (HCPs). Methods: We compare results from two social listening studies using the same methodology to capture global, public, social media (i.e., X, Reddit, etc.) posts from Period 1: 5/1/2019 – 5/30/2021 and Period 2: 12/1/2022 – 12/31/2023, using technologies such as web crawlers and data partnerships. Sociolinguists analyzed the posts in the native language using qualitative and quantitative methods. Results: Overall, total social media posts describing the disease experience of individual patients with IgAN from Period 1 to Period 2 increased (1,024 to 1,165) and was comprised of identifiable patients (902, 1,105), caregivers (102, 39), and HCPs (20, 21) respectively. Posts increased from Japan (+106), the United Kingdom (+40), South Korea (+32), Germany (+37), and Canada (+19) and decreased from the United States (-59) and France (-39). Posts about symptoms were similar (229 to 248); treatments increased (627 to 723) and impact decreased (400 to 233). Post characteristics over time are detailed in Table 1 below. Conclusion: This comparative analysis of real-world social media posts assessed discussion trends of patients with IgAN to better understand their experiences. Social media conversations increased over time and their global distribution, patient characteristics, and content changed. This analysis demonstrates that patient experiences of IgAN are not static; regular updates to detect changes in patient experiences of disease manifestations, disease impact, and treatment of IgAN can help inform future research studies. Funding: Commercial Support - Otsuka Pharmaceutical Development and Commercialization, Inc. Table 1: Post Characteristics Over Time from Period 1 to Period 2 - Post Characteristic* Period 1 (%) Period 2 (%) Status Seeking DiagnosisNewly DiagnosedIn TreatmentKidney Failure 16233229 812773 Symptoms HematuriaProteinuriaFatiguePainHigh Blood Pressure 344212516 35338127 Impact EmotionalPhysicalTreatment BurdenCaregiver/Relationship 30172417 572232 *Single post could mention more than one characteristic
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".