Intravenous immune globulin and thromboembolic adverse events in patients with hematologic malignancy
Bibliographic record
Abstract
In patients with hypogammaglobulinemia secondary to chronic lymphocytic leukemia (CLL) or multiple myeloma (MM), intravenous immune globulin (IVIg) may be administered to reduce the risk of infection. Since 2013, IVIg products have carried a boxed safety warning about the risk of thromboembolic events (TEEs), with TEEs reported in 0.5% to 15% of patients treated with IVIg. In this retrospective cohort study of older patients with CLL or MM identified from the Surveillance, Epidemiology, and End Results-Medicare Linked Database, we assessed rates of clinically serious TEEs in 2724 new users of IVIg and a propensity-matched comparison group of 8035 nonusers. For the primary end point, arterial TEE, we observed a transient increased risk of TEE during the day of an IVIg infusion and the day afterward (hazard ration = 3.40; 95% confidence interval [CI]: 1.25, 9.25); this risk declined over the remainder of the 30-day treatment cycle. When considered in terms of absolute risk averaged over a 1-year treatment period, the increase in risk attributable to IVIg was estimated to be 0.7% (95% CI: -0.2%, 2.0%) compared with a baseline risk of 1.8% for the arterial TEE end point. A statistically nonsignificant risk increase of 0.3% (95% CI: -0.4%, 1.5%) compared with a baseline risk of 1.1% was observed for the venous TEE end point. Further research is needed to establish the generalizability of these results to patients receiving higher doses of IVIg for other indications.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".