Prefilled syringes for immunoglobulin G (IgG) replacement therapy: clinical experience from other disease settings
Bibliographic record
Abstract
INTRODUCTION: Ready-to-use prefilled syringes for drug delivery are increasingly used across a broad spectrum of clinical specialties. For patients with primary immunodeficiencies manifesting as antibody deficiencies, immunoglobulin G (IgG) replacement therapy (IgRT) by subcutaneous administration is an established treatment modality. Expanding IgRT administration options through the introduction of prefilled syringes may further improve its utility. AREAS COVERED: Here, we collate experience with prefilled syringes from other clinical settings to inform on their practicality and suitability for IgRT. In addition to discussing drug characteristics such as stability, pharmacokinetics, and efficacy, we focus on treatment delivery, physician/patient experience, costs, and the importance of education for the use of prefilled syringes. EXPERT OPINION: Perceived benefits of prefilled syringes include accurate dosing, sterility, and reduced treatment time, while offering patients greater choice, convenience, and ease-of-use. Our review of clinical experience with prefilled syringes supports this consensus. Relatively few studies directly compare prefilled syringes with conventional administration, and robust studies of cost-effectiveness and health-related quality of life are needed on a drug-by-drug basis. Growth in the availability of prefilled syringes will continue, encouraged by the importance of patient choice and treatment convenience, toward the goal of individualized treatment regimens and improved quality of life.
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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 | 0.003 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".