Immunoglobulin treatment and clinical outcomes: data from the Ontario Immunoglobulin Treatment program multicenter case registry
Bibliographic record
Abstract
Background: The therapeutic use of immunoglobulin (IG) is increasing and accounts for the largest expenditure in the Canadian Blood Services budget. However, more granular data on IG utilization is limited. Objective: To describe IG treatment indications, dosing characteristics, and clinical outcomes in patients enrolled in the Ontario IG Treatment (ONIT) program, a government-funded pilot clinical program with a case registry. Methods: A longitudinal descriptive study was conducted on ONIT registry participants from June 1, 2020 to March 31, 2024. Results: Six hundred ninety-three consenting participants were included; 429 (61.9%) were female; median [Q1, Q3] age was 62 [47, 71] years; 47 (6.8%) passed away during the study period. Of 693, 658 (94.9%) were receiving IG treatment: 544 (82.7%) on SCIG and 114 (17.3%) on IVIG. Treatment indications were primary immune deficiency (PID) (299, 43.1%), secondary immune deficiency (SID) (348, 50.2%), and immune-mediated disease (IMD) (46, 6.7%). The median dose was 0.48 [0.42, 0.57] and 0.52 [0.44, 0.64] g/kg/4 weeks, for SCIG and IVIG, respectively. Seventy-three patients transitioned from IVIG to SCIG, with the dose adjusted to clinical response. The IVIG:SCIG conversion ratios were 1:1, 1:0.9, and 1:1.2 for PID, SID, and IMD, respectively. Only 33 (5.0%) stopped IG during the study. There was a 78.4% reduction in infections and over 90% reduction in emergency room visits and hospitalizations in PID and SID. Most patients (89.4%) reported improved health after starting IG therapy. Conclusion: The study provides insights into the current landscape of IG utilization, which may inform health system research and support healthcare delivery planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".