The impact of institutional measures on optimal use of intravenous immunoglobulin
Bibliographic record
Abstract
BACKGROUND: Intravenous immunoglobulin (IVIG) shortage represents an emerging issue in transfusion medicine. Limited data are available to determine effective strategies for optimal use. The objective of this retrospective observational study was to determine the impact of institutional measures on IVIG use at a large academic center. METHODS: IVIG infusions from November 26, 2018 to September 25, 2022 were categorized according to their appropriateness (Recommended, Option of treatment, or Unrecommended), based on provincial guidelines, and separated into three phases: Reference, Transition, and Post-Implementation phases, the latter following the adoption of restrictive measures, including mandatory standardized order forms, a blood bank gatekeeping strategy, and the creation of a stewardship committee. RESULTS: A total of 5431 IVIG infusions were administered to 544 patients, accounting for 295,033 g. The most common indication categories were neurology (30.4%), immunology (29.0%), and hematology (17.4%). From Reference to Post-Implementation phase, IVIG infusions decreased from 2275 to 2000 with unrecommended indications dropping from 9.5% to 7.4% (p = 0.01), and a global reduction of 23.0% (from 131,163 g to 100,936 g of IVIG). Decrease in chronic immunomodulation accounted for 48.3% of total reduction (14,610 g of 30,227 g), whereas single-use immunomodulation, 40.5% (12,237 g of 30,227 g). Moreover, an absolute reduction of 16.9% was observed in orders exceeding the recommended doses (20.8% to 3.9%; p < 0.0001). Together, the unrecommended and excessive IVIG doses decreased from 19,975 g (15.2%) to 6670 g (6.6%). CONCLUSIONS: A global reduction in IVIG use and a preferential decrease in the unrecommended orders were observed, most likely attributable to the bundle of restrictive strategies implemented.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".