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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".