Expert consensus statement on the treatment of immune-mediated inflammatory diseases with Janus kinase inhibitors: 2024 update
Bibliographic record
Abstract
In light of the introduction of new Janus kinase inhibitors (JAKi), new indications for JAKi and recent safety considerations that have arisen since the preceding consensus statement on JAKi therapy, a multidisciplinary taskforce was assembled, encompassing patients, health care professionals, and clinicians with expertise in JAKi therapy across specialties. This taskforce, informed by two comprehensive systematic literature reviews, undertook the objective to update the previous expert consensus for using JAKi developed in 2019. The taskforce deliberated on overarching principles, indications, dosage and comedication strategies, warnings and contraindications, screening protocols, monitoring recommendations, and adverse effect profiles. The methodology was based on the European Alliance of Associations for Rheumatology standard operating procedures, with voting on these important elements. Furthermore, an updated research agenda was proposed. The task force did not address when a JAKi should be prescribed but rather considerations once this decision has been made. This update aimed to equip clinicians with the necessary knowledge and guidance for the efficient and safe administration of this expanding and significant class of drugs.
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 | 0.044 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".