Rituximab utilization for approved and off‐label nononcology indications and patients’ experiences with the Patient Alert Card
Bibliographic record
Abstract
) in autoimmune conditions and (2) patients' receipt and knowledge of the Patient Alert Card (PAC), a risk minimization measure for progressive multifocal leukoencephalopathy (PML) and serious infections. Anonymized patient data were collected from infusion centers in Europe from December 2015 to July 2017. Adults receiving rituximab in the same centers were provided a self-administered survey. Outcomes included patterns of off-label rituximab use for nononcology indications, and evaluation of patients' receipt and knowledge of the PAC and its impact. Of 1012 patients in the retrospective chart review, 70.2% received rituximab for rheumatoid arthritis or granulomatosis with polyangiitis/microscopic polyangiitis, and 29.8% received rituximab off label. Among 524 survey participants, 32.8% reported receiving the PAC, 59.3% reported not receiving the PAC and 7.9% did not know whether they received the PAC. A total of 72.4% of patients reported that they were unaware that some patients receiving rituximab experience PML. A higher proportion of PAC recipients identified PML as a potential risk of rituximab than nonrecipients (37.8% vs 19.9%); 58.3% of PAC recipients had poor awareness of PML. Most PAC recipients (90.0%) and nonrecipients (85.5%) correctly answered that they should seek medical attention for infection symptoms. In conclusion, approximately 30% of patients received off-label rituximab. Most patients reported not receiving the PAC or having knowledge of PML but demonstrated understanding of the recommended action in the event of infection symptoms, regardless of PAC receipt.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".