Development, implementation and impact of an immunoglobulin stewardship programme in Saskatchewan, Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Intravenous immunoglobulin (IVIG) is one of the most costly and limited-supply blood products. Judicious use of this therapy is important to ensure a continued supply is available for patients in need. The Saskatchewan IG Stewardship Program was initiated to monitor and reduce inappropriate IG use. MATERIALS AND METHODS: The Program was developed and implemented through the collaborative efforts of a multidisciplinary, inter-organizational team. Funding was provided from provincial organizations to create new positions within the Program and to support stakeholder engagement throughout the process of implementation. Data were collected from local and national databases regarding the amount of IVIG used and appropriateness of orders based on published criteria. RESULTS: Over 20 months, the Program helped to reduce unnecessary IVIG use from pre-intervention levels by more than 20%. Interventions from nurse navigators alone reduced inappropriate IVIG use by 2.6%. During the 20-month period following Program initiation, more than 4 million CAD less was spent on IVIG compared with the previous 20 months. CONCLUSION: The Saskatchewan IG Stewardship Program has led to more appropriate IVIG use across the province, more effective preservation of this limited healthcare resource, and cost savings that more than cover the cost of administering the Program.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".