Clinical outcomes of immunoglobulin treatment for patients with secondary antibody deficiency: Data from the Ontario immunoglobulin treatment case registry
Bibliographic record
Abstract
BACKGROUND: Despite the increasing number of cases of secondary antibody deficiency (SAD) and immunoglobulin (Ig) utilization, there is a paucity of data in the literature on clinical and patient-reported outcomes in this population. OBJECTIVE: To describe immunoglobulin utilization patterns, clinical and patient-reported outcomes in patients with SAD on immunoglobulin replacement therapy (IgRT). METHODS: A cross-sectional study of patients with secondary antibody deficiency enrolled in the Ontario Immunoglobulin Treatment (ONIT) Case Registry from June 2020 to September 2022 was completed. Demographics, comorbidities, indications for immunoglobulin treatment, clinical infections at baseline and post IgRT, and patient-reported outcomes were collected and analyzed. RESULTS: There were 140 patients (58 males; 82 females; median age 68) with SAD during the study period; 131 were on subcutaneous Ig (SCIG) and 9 were on intravenous Ig (IVIG). The most common indication was chronic lymphocytic leukemia (CLL) (N = 52). IgRT reduced the average annual number of infections by 82.6%, emergency room (ER) visits by 84.6%, and hospitalizations by 83.3%. Overall, 84.6% of patients reported their health as better compared to before IgRT. Among those patients who switched from IVIG to SCIG (N = 35), 33.3% reported their health as the same, and 62.9% reported their health as better. CONCLUSIONS: This study demonstrates that IgRT significantly improved clinical outcomes and patient-reported general health state in patients with SAD. This study also further supports the use of SCIG in patients with SAD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".