Trends in IVIG use at a tertiary care Canadian center and impact of provincial use mitigation strategies: 10‐year retrospective study with interrupted time series analysis
Bibliographic record
Abstract
BACKGROUND: Intravenous immunoglobulin (IVIG) is a fractionated plasma product used to treat a range of autoimmune or inflammatory conditions, as well as immunodeficiency. Demand for this high-cost product is increasing worldwide. Understanding historical changes in IVIG use is important for inventory management and demand forecasting as well as for the development of initiatives aimed at optimizing blood product use. STUDY DESIGN AND METHODS: This was a 10-year retrospective cohort study of all patient encounters involving an IVIG transfusion from 2007 to 2016 at a four-site tertiary care hospital in Ontario, Canada. IVIG use was reported, including number of hospital encounters and amounts of IVIG prescribed. An interrupted time series analysis was performed to evaluate temporal changes in product use coinciding with the release of 2009-2010 provincial initiatives to optimize IVIG. RESULTS: A total of 1,658,159.50 g of IVIG was administered from 2007 to 2016. Total annual volume administered initially decreased after implementation of new policies (-2032 g/quarter). The number of IVIG patient encounters also decreased (-49.8 encounters/quarter) but was mirrored by an increase in the total volume administered per patient encounter (+0.88 g/quarter). Use increased 820 g/quarter from 2013 to 2016 but was 21% lower than projected before implementation of provincial policies. CONCLUSION: Trends in IVIG use show ongoing increases in the number of patients treated with the product. Development and implementation of provincial initiatives to optimize IVIG use coincided with significant short-term changes at a large tertiary care hospital. Novel initiatives aimed at dose minimization and prescription rationalization for this therapy are needed on local as well as larger scales.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".