e48 Effects of baricitinib on haematological laboratory parameters in patients with rheumatoid arthritis
Bibliographic record
Abstract
Background: Baricitinib (BARI), an oral, Janus kinase (JAK) 1/2 inhibitor, is used for treating adults with moderate to severe rheumatoid arthritis (RA). RA is associated with an increased neutrophil and platelet count, and decreased lymphocyte count. JAK2 signaling pathway is involved in production of erythrocytes and other blood cells. Methods: To summarise changes in absolute neutrophil counts (ANC), absolute lymphocyte counts (ALC), platelet counts, and haemoglobin (Hgb), and associated adverse events, with BARI treatment. Data were pooled from completed Phase 1/2/3 studies and an extension study. Results: BARI treatment was associated with a decrease in ANC and an increase in ALC and platelets, which stabilised over time and returned to baseline with prolonged treatment or treatment discontinuation. Incidence of neutropaenia (<1000 cells/mm3) was rare (<1%) and was not associated with higher risk of overall or serious infections. Lymphopaenia was associated with slightly higher rate of overall infections (Table 1). More BARI 4-mg (2.3%) as compared to placebo-treated (1.3%) patients had platelet count ≥600x109/L. In 6-study placebo-controlled set (0-24 weeks), 5 BARI 4-mg-treated patients (vs 0 placebo-treated) had deep vein thrombosis (DVT) and/or pulmonary embolism (PE). Incidence rate of overall and serious DVT/PE in ALL BARI-RA set remained low at 0.5 and 0.3 per 100 patient-years, respectively. The proportion of patients with high platelet levels (≥600x109/L) was comparable between patients with DVT/PE vs those without DVT/PE (at baseline: 0 vs 0.5%; post-baseline: 6.5% vs 3.3%). With long-term BARI treatment, Hgb levels decreased transiently before returning to levels slightly higher than baseline at Week-52. Incidence of treatment-emergent (TE) shifts (normal to below the lower limit of normal) in Hgb was comparable between BARI 4-mg and placebo treatment groups (29.3% vs 25.8%). Incidence of severe TE shifts in Hgb (grade <3 to grade ≥3: <8 and ≥6.5 g/dL) was low across all treatment groups (<0.5%).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".