National Immunoglobulin Replacement Expert Committee Recommendations
Bibliographic record
Abstract
The use of immunoglobulin therapy has grown steadily over the past 3 decades, mainly due to the increased awareness and expansion of indications. This limited resource is now shared between patients with immunodeficiency disorders who need it as replacement therapy, and individuals who suffer a variety of autoimmune or inflammatory disorders. While alternative therapies exist for the latter diseases, immunodeficient patients are dependent upon this treatment for life. Due to the long-term cost burden of this treatment on healthcare systems, healthcare providers have attempted to moderate its use but are frequently seeking evidence from experts in the field. This raised the critical need for recommendations from experts. To this end, a group a Canadian immunologists representing all regions of the country have formed a panel, the National Immunoglobulin replacement Expert Committee (NIGEC), and formulated a set of unanimously agreed upon recommendations for the use of immunoglobulin replacement in primary immunodeficiency.
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 | 0.028 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.015 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".