Immunoglobulin utilization in Canada: a comparative analysis of provincial guidelines and a scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Canada has high immunoglobulin (IG) product utilization, raising concerns about appropriate utilization, cost and risk of shortages. Currently, there is no national set of standardized IG guidelines, and considerable variations exist among the existing provincial guidelines. The aims of this study were: (1) to compare the existing Canadian provincial guidelines on the use of IG products to identify their consistencies and differences and (2) to examine the existing research in Canada on IG supply and utilization following the establishment of IG guidelines to understand the scope of research and pinpoint the gaps. METHODS: A comparative analysis accounted for the differences across provincial IG guidelines. We highlighted similarities and differences in recommendations for medical conditions. A scoping review of citations from MEDLINE, PubMed, Scopus and Embase databases was conducted for studies published from January 01, 2014, to April 12, 2023. RESULTS: While provincial guidelines represented a considerable overlap in the medical conditions delineated and relatively uniform dose calculations, numerous differences were observed, including in recommendation categories, provision of pediatric dosing, and divergent recommendations for identical conditions based on patient demographics. The scoping review identified 29 studies that focused on the use of IG in Canada. The themes of the studies included: IVIG utilization and audits, the switch from IVIG to SCIG, patient satisfaction with IVIG and/or SCIG, the economic impact of self-administered SCIG versus clinically administered IVIG therapy, and the efficacy and cost-effectiveness of alternative medications to IG treatment. CONCLUSION: The differences in guidelines across provinces and the factors influencing IVIG/SCIG use, patient satisfaction, and cost savings are highlighted. Future research may focus on clarifying costs and comparative effectiveness, exploring factors influencing guideline adherence, and evaluating the impact of updated guidelines on IG use and patient outcomes.
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 | 0.023 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.043 | 0.093 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| 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".