A pilot study of quality of life, mood, sleepiness and fatigue in patients with primary humoral immunodeficiency transitioning to subcutaneous immunoglobulin therapy
Bibliographic record
Abstract
Immunoglobulin replacement therapy is standard of care for patients with primary humoral immunodeficiency [ 1 ]. Compared with intravenous immunoglobulin (IVIG), subcutaneous immunoglobulin (SCIG) offers comparable efficacy, lower costs and reduced systemic reactions [ 2 , 3 ]. However, little is known about effects on quality of life when patients transition from IVIG to SCIG. It was our objective to assess changes in quality of life, mood, sleepiness and fatigue in patients transitioning from IVIG to SCIG. Adult patients with common variable immunodeficiency or X-linked agammaglobulinemia transitioning from IVIG to SCIG were invited to participate in this prospective, open-label, pilot study. At least one set of Short-Form 36 Health Survey (SF-36), Profile of Mood States (POMS), Epworth Sleepiness Scale (ESS) and nighttime sleep questionnaires was administered prior to the final IVIG infusion. These were repeated monthly for 3 months following the transition. Magnitude of change was estimated between IVIG trough and final SCIG steady-state data. Statistical significance was determined using linear mixed models for repeated measures with Kenward-Rogers correction. Twenty-seven patients were included in the analysis. Two of eight SF-36 quality of life domains showed significant improvement: role limitations due to physical health (p = 0.01) and emotional problems (p = 0.04). Two of six POMS mood subscales significantly improved: depression (p = 0.03) and anger (p = 0.04). One of six POMS mood subscales (tension, p = 0.08) and POMS total mood disturbance scores (p = 0.09) trended towards improvement. No significant changes were noted in ESS or nighttime sleepiness scores. Patients transitioning from IVIG to SCIG for treatment of primary antibody immunodeficiency showed significant improvement in several quality of life and mood subscales. A larger study verifying these findings could encourage patients to switch to SCIG self-administration, producing quality of life benefits while decreasing health care costs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".