Immune thrombocytopenia (ITP) World Impact Survey (iWISh): Patient and physician perceptions of diagnosis, signs and symptoms, and treatment
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is now well-known to reduce patients' health-related quality of life. However, data describing which signs and symptoms patients and physicians perceive as having the greatest impact are limited, as is understanding the full effects of ITP treatments. I-WISh (ITP World Impact Survey) was an exploratory, cross-sectional survey designed to establish the multifaceted impact of ITP, and its treatments, on patients' lives. It focused on perceptions of 1507 patients and 472 physicians from 13 countries regarding diagnostic pathway, frequency and severity of signs and symptoms, and treatment use. Twenty-two percent of patients experienced delayed diagnosis (caused by several factors), 73% of whom felt anxious as a result. Patients rated fatigue among the most frequent, severe symptom associated with ITP at diagnosis (58% most frequent; 73% most severe), although physicians assigned it lower priority (30%). Fatigue was one of the few symptoms persisting at survey completion (50% and 65%, respectively) and was the top symptom patients wanted resolved (46%). Participating physicians were experienced at treating ITP, thereby recognizing the need to limit corticosteroid use to newly-diagnosed or first-relapse patients and espoused increased use of thrombopoietin receptor agonists and anti-CD20 after relapse in patients with persistent/chronic disease. Patient and physicians were largely aligned on diagnosis, symptoms, and treatment use. I-WISh demonstrated that patients and physicians largely align on overall ITP symptom burden, with certain differences, for example, fatigue. Understanding the emotional and clinical toll of ITP on the patient will facilitate shared decision-management, setting and establishment of treatment goals and disease stage-appropriate treatment selection.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".