Educational and Emotional Needs of Patients with Myelodysplastic Syndromes: An AI Analysis of Multi-Country Social Media
Bibliographic record
Abstract
Myelodysplastic syndromes (MDS) comprise a heterogeneous group of myeloid malignancies characterized by high symptom burden and limited treatment options. A central challenge to caring for patients with MDS is assessing their needs throughout the different phases of the disease. Patients and caregivers frequently consult online sources to address informational and emotional support needs. We conducted a social listening analysis of publicly available online forums to identify unmet needs of patients with MDS and their caregivers in the USA, the UK, Spain, Canada, France, and China. We used artificial intelligence (AI) and natural language processing (NLP) to group categories of posts into seven overarching motivations for online engagement (Clinical, Emotional, Treatments, Transplant, Education and Logistics, Physical, and Diet and Lifestyle). Posts from the USA and China commonly discussed clinical topics such as MDS diagnosis, disease monitoring, and progression. Posts from Canada and France were frequently about treatments and treatment options. Emotional concerns were key drivers of posts from Canada, Spain, and the UK. Additionally, we also identified topics associated with negative language at key phases during the treatment experience where patients and caregivers exhibited increased online engagement, revealing educational and emotional support gaps at the time of diagnosis, when patients are deciding between treatment options, and when treatment options fail. In this research, based on social media listening analyzed using AI and NLP, potential information gaps and unmet needs among patients with MDS were identified. Addressing these gaps through targeted patient education and guidance to emotional support options during these phases could reduce the disease burden and emotional distress experienced by patients with MDS. To better understand patients’ needs, we conducted a social listening analysis of frequently discussed topics on public online MDS forums in the USA, UK, Spain, Canada, France, and China. We used artificial intelligence and natural language processing to group motivations for online engagement into seven themes: Clinical, Emotional, Treatments, Transplant, Education/Logistics, Physical, Diet/Lifestyle. Forum posts from the USA and China usually discussed clinical topics like diagnosis, disease monitoring, and disease progression. Posts about treatments and exploring treatment options were a priority in Canada and France. Emotional concerns were a key focus among posts from Canada, France, Spain, and the UK. We also identified topics that contained strong negative sentiment at key milestones where patients/caregivers had increased online engagement. This revealed that educational/emotional support was most insufficient at the time of diagnosis, when patients are deciding between treatments, and when treatments fail. Addressing these concerns through improving patient education and offering guidance to emotional support options during specific phases of the disease journey could help manage the impact of patients’ symptoms, improve their disease and treatment experience, and thus potentially enhance the quality-of-life of patients with MDS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".