A global analysis of the use of immunoglobulin, shortages in supply, and mitigating measures: A survey of hospital providers (a <scp>BEST</scp> Collaborative study)
Bibliographic record
Abstract
BACKGROUND: Immunoglobulin (IG) therapy is widely used to treat primary and secondary immune deficiencies and as immunomodulatory agent for various disorders. There is great concern that shortages of IG may rise, potentially affecting medical treatment options. STUDY DESIGN AND METHODS: An international survey was developed to study how intravenous immunoglobulins (IVIGs) are used and managed within hospitals in case of shortages. Study data were collected and managed using REDCap electronic data capture tools hosted by the Biomedical Excellence for Safer Transfusion (BEST) Collaborative. The survey was directed to hospital pharmacists and blood bank transfusion professionals and disseminated through members of the BEST Collaborative network. RESULTS: Survey respondents from institutions in the USA, Canada, Europe, Japan, and Australia (n = 13) confirmed that the primary specialties utilizing IG are neurology, hematology, and immunology. More than 60% of respondents reported IG supply shortages, but mitigation strategies were not well developed. DISCUSSION: As IG is the leading driver in plasma demand, more studies are needed to understand current and future demand for IG from the clinical perspective. Necessity lies in establishing clinical guidance to address shortages.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".