Evaluation of immunoglobulin replacement therapy in secondary immunodeficiency at three British Columbia hospitals
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Immunoglobulin (Ig) usage has ongoing shortage concerns. Secondary immunodeficiencies (SIDs) account for a major proportion of usage of Igs in Canada. We audited Ig usage in patients with SID at three British Columbia hospitals to determine whether more stringent local guidelines are necessary. MATERIALS AND METHODS: A retrospective chart review was performed for patients who had Ig ordered between 1 January 2018 and 31 December 2019 for any SID indication. Cohorts were stratified into chronic and new users, and the Australian BloodSTAR guidelines were used as the benchmark at the time of conception. Having an eligible primary diagnosis, meeting SID criteria, an appropriate dosage and follow-up immunoglobulin G (IgG) levels encompassed appropriate usage. RESULTS: There were no demographic differences between chronic (N = 81) and new (N = 33) cohorts. The new cohort had a higher rate of appropriate usage (45.7% vs. 66.7%, p = 0.06). The most common reason for inappropriate usage in both groups was the lack of follow-up IgG level at 6 or 12 months. Factors, displayed by relative risk (RR), associated with appropriateness included the dispensing hospital (RR: 6.60), use of subcutaneous Ig (RR: 3.84), having an IgG level before starting therapy (RR: 3.51) and documentation of clinical benefit (RR: 4.70). CONCLUSION: There are high rates of inappropriate Ig usage in SID patients in both new and chronically treated groups. More stringent local guidelines and processes for assessing initial and ongoing Ig replacement are warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".