<p>The Development of a New Questionnaire to Measure the Burden of Immunoglobulin Treatment in Patients with Primary Immunodeficiencies: The IgBoT-35</p>
Bibliographic record
Abstract
PURPOSE: To describe the development and psychometric testing of a new questionnaire to measure the burden of immunoglobulin treatment (Ig) from the perspective of patients with primary immunodeficiencies (PID). PATIENTS AND METHODS: An online, cross-sectional survey was administered to PID patients across 10 countries (nine European and Canada) who were receiving either intravenous (IVIg) or subcutaneous (SCIg) immunoglobulin therapy. The range and distribution of the responses (ie, levels of missing data, floor and ceiling effects), exploratory factor analysis (using factor loadings of 0.4 or greater) and measures of internal consistency reliability (ie, Cronbach's alpha coefficient, inter-item and item-total correlations) were used to identify the domain and item pool. RESULTS: In total, 472 patients completed the questionnaire, of which 395 were included in the analysis (32% underwent IVIg and 67% underwent SCIg). The final instrument contained 34 items across eight domains of treatment burden (time, organisation and planning, leisure and social, interpersonal relationships, employment and education, travel, consequences of treatment and emotional) and an additional Ig treatment burden global question at the end of the measure. All the scales achieved good internal reliability (Cronbach's alpha coefficient ranged from 0.70 to 0.85) and, with the exception of one item exceeded the minimum threshold of 0.35 for item-total correlations. Treatment burden was lower than anticipated across the different treatment routes and countries, although overall was more burdensome for patients undergoing IVIg compared to SCIg treatment. CONCLUSION: The IgBoT-35 appears to be a reliable, patient-generated questionnaire and may help to identify more individualised and preferred therapies for the PID patient when used in clinical practice. A new survey with a sample of US patients is currently being undertaken to further establish its validity and conceptual model. The overall Ig burden of treatment scores appeared to be low. PID patient preferences are important to guide treatment decisions and ensuring patients receive the right treatment at the right time.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".