Intravenous immunoglobulin utilization in the Canadian Atlantic provinces: a report of the Atlantic Collaborative Intravenous Immune Globulin Utilization Working Group
Bibliographic record
Abstract
BACKGROUND: Intravenous immunoglobulin (IVIG) use for labeled and unlabeled indications is growing steadily. By use of a collaborative regional strategy, baseline IVIG usage and appropriateness of utilization were determined for Atlantic Canada. The effectiveness of strategies designed to optimize utilization was also studied. STUDY DESIGN AND METHODS: A regional working group was created to monitor IVIG utilization for a 2-year period in the four Canadian Atlantic Provinces. A registry of IVIG was created. Assessment of indication appropriateness was determined with national and provincial guidelines along with expert clinical opinion. To optimize IVIG use, IVIG guidelines and feedback reports were distributed to stakeholders. Appropriateness of IVIG use was compared over the course of the study. RESULTS: The leading indications for IVIG use were idiopathic thrombocytopenic purpura (17.3%), primary immune deficiency conditions (14.9%), and chronic idiopathic demyelinating polyneuropathy (11.8%). The leading prescribing specialists were neurologists (32.2%) and hematologists (26.1%). A total of 37.1 percent of IVIG usage was in accordance with labeled indications. After optimization strategies were implemented, there was little change in labeled use. There was a 4.2 percent decrease in unlabeled use not supported by evidence (p<0.001). CONCLUSIONS: A regional collaborative strategy for monitoring IVIG use was established. Most of the IVIG use was for labeled or appropriate indications. The majority of unlabeled use was supported by the medical literature. Strategies to optimize IVIG utilization were associated with a decrease in inappropriate IVIG use and a plateau in IVIG utilization compared to the rest of the country.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".