Home gammaglobulin therapy: a patient survey of intravenous and subcutaneous options in Canada
Bibliographic record
Abstract
Introduction: For close to half a century immunoglobulin replacement therapy has been the main therapy for patients unable to produce functioning antibodies. To date, both subcutaneous (SC) and intravenous delivery methods have been successful at effectively and safely replacing immunoglobulin. Home intravenous and SC therapy programs have been established and have gained attention, but the true motivation and frequency of switching from traditional hospital-based treatment to these alternatives remains unknown. This study aims to determine the willingness of patients in Canada to switch to a home-based gammaglobulin treatment program by quantifying related experiences and preferences. Methods: A cohort of 169 patients in Ontario currently on hospital-based intravenous immunoglobulin (IVIG) replacement therapy (referral centers or community hospitals) were sent a 2.5 page survey consisting of 25 questions. Data were collected and statistically analyzed using Fisher, χ2, and McNemar tests, where P < 0.05 was considered statistically significant. Results: Ninety-one patients responded and most agreed to consider home therapy regardless of the administration route, based on recommendations from an immunologist (IVIG, P = 0.006; SC, P < 0.001). Patients preferred switching to home IVIG rather than to SC (P = 0.01), but their concerns regarding home healthcare costs were more prominent with IVIG (P = 0.01). The main concern with current intravenous therapy was the overall loss of time (P = 0.0001), whereas for home therapy it was the loss of supervision (P = 0.0009) and possible associated costs. Patients considered home treatment more convenient, as it is less time consuming (P = 0.01), and this was perceived as an improvement in quality of life (P = 0.001). It was considered less convenient because it may be unsafe and (or) more expensive. Conclusion: This survey demonstrates that home intravenous therapy maybe the preferred option for patients with antibody deficiency in Ontario, provided this decision was supported by a specialist in the field, secured supervision was available, and it was not associated with personal expenses. Statement of novelty: The first study to examine patient willingness to try a new route of gammaglobulin administration at home.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".