P.056 Optimizing IVIg Use for Neuromuscular Conditions in British Columbia, Canada – Targeting High and Chronic User Groups
Bibliographic record
Abstract
Background: Neuromuscular conditions account for 1/3 of IVIg use in BC and costs over $10 million annually. Since 2013, the BC Neuromuscular Review Panel has developed diagnostic and treatment algorithms for the use of IVIg. A framework was created to review high dose and chronic users. Methods: Utilizing Central Transfusion Registry data, all patients treated with IVIg for approved neuromuscular conditions (CIDP, MG, MMN) since April 1, 2013 were identified. Annual cohorts for patients using higher than usual dose and chronic use (>3 years) were established, and evaluated annually. Patient specific recommendations were made. Results: The initial cohort identified 38 high users of 377 patients receiving IVIg. 27 appropriate, 9 “not appropriate”. Subsequent cohorts showed a decrease in number of patients receiving inappropriate IVIg doses. In BC there has been a 36% increase in neuromuscular patients treated with IVIg (377 in 2013/14 to 512 in 2016/17). Despite this, IVIg the program has effectively reduced the annual grams/patient from 516 gm/patient in 2013/14 to 489 gm/patient in 2016/17. Conclusions: The BC Neuromuscular IVIg Review confirms that the majority of IVIg use is appropriate. Following yearly cohorts of chronic and high dose users helps optimize IVIg use, which may lead to improved patient care.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".