Cutaquig® Is Well Tolerated in Immunodeficient Patients Who Did Not Tolerate Other Subcutaneous Immunoglobulin Products
Bibliographic record
Abstract
Objective: Subcutaneous immunoglobulin (SCIG) treatment is generally tolerable, but some patients may experience adverse events to one or more SCIG products. We investigated whether 16.5% Cutaquig® treatment offered a tolerable and safe alternative treatment for immunodeficient patients. Methods: A one-year prospective cohort study was conducted at a single center in Ottawa, Canada. Adult immunodeficient patients who reported previous intolerability, adverse events, or other difficulty to other 20% SCIG product(s) were recruited to start on 16.5% Cutaquig®. Treatment tolerability, safety, and quality of life were observed and described. Results: Seven out of ten patients tolerated Cutaquig®. There were no serious or severe adverse events related to the treatment. Three moderate infections were reported (two urinary tract infections and one injection site infection). The mean serum IgG level at the end of the study was comparable to baseline levels recorded before the study: 9.6 ± 4.5 vs. 7.6 ± 4.3 g/L, p = 0.07. The overall health and health domain changes in the SF-36 and quality of life tests using the EQ visual analog scale improved by 21.5% (p = 0.38), 16.7% (p = 0.29), and 7.7% (p = 0.23), respectively. Conclusions: Cutaquig® may be used as an alternative treatment option for patients who did not tolerate 20% SCIG products.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".