Obstetrical use of intravenous immunoglobulin: A single-centre retrospective study
Bibliographic record
Abstract
INTRODUCTION: Intravenous immunoglobulin is widely used for various conditions but faces challenges such as limited supply, high cost, and substantial off-label use. Obstetrical intravenous immunoglobulin use remains underexplored, despite its relevance to maternal and neonatal care and resource management. METHODS: This single-center retrospective cohort study examined intravenous immunoglobulin administration in 136 pregnancies (122 patients) from 2007-2020, focusing on adherence to Health Canada licensed indications and Ontario Immunoglobulin Utilization Management Guidelines. RESULTS: Maternal thrombocytopenia (56.6 %) and treatment for fetal/neonatal alloimmune thrombocytopenia (16.2 %) were the most common indications, accounting for 16.9 % and 64.3 % of total intravenous immunoglobulin volume, respectively. Intravenous immunoglobulin use represented 1.6 % of the center's total consumption during the study period, with notable non-adherence to guidelines in 38.2 % (Health Canada) and 17.6 % (provincial guidelines) of pregnancies. CONCLUSION: Findings highlight the need for optimized intravenous immunoglobulin use in obstetrics and future research to ensure safety, efficacy, and evidence-based guidance in clinical practice and policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".