Rapid push: new opportunities in subcutaneous immunoglobulin replacement therapy
Bibliographic record
Abstract
Primary antibody deficiencies (PAD) are the largest group of primary immunodeficiency diseases (PID), affecting patients at various age. Affected individuals are extremely prone to serious and recurrent infections, which can led to tissue damage and premature death. Many patients require regular, life-long prophylactic treatment with immunoglobulin. For years the leading route has been intravenous infusion of immunoglobulin (IVIG). Despite its clinical benefits, IVIG is associated with some inconveniences such as travelling to hospital, difficulties obtaining vein access and systemic side effects. The alternative method to IVIG is subcutaneous immunoglobulin infusion (SCIG), introduced in the 1990s. It has become increasingly popular in recent years. This route does not require venous access, has a really low risk of systemic side effects and improves quality of life. SCIG can be administered either via programmable pump or rapid push infusion. Despite similar effectiveness and risk of adverse events the rapid push route seems to be safe and viable, providing more self-control, treatment flexibility, independence and opportunities for treatment satisfaction. Rapid push immunoglobulin infusion has recently become more popular in the USA and Canada. This route should be considered as an alternative possibility of replacement immunoglobulin therapy in patients with immunodeficiency in Europe.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".