Monitoring the transition of patients on biologics in rheumatoid arthritis: Consensus guidance for pharmacists
Bibliographic record
Abstract
BACKGROUND: Recent approvals for novel agents such as the small molecule Janus kinase inhibitors (JAKi), combined with the advent of biosimilars has widened the gamut of available therapeutic options in the treatment of rheumatoid arthritis (RA). This combined with the introduction of mandatory non- medical switches to biosimilars in some jurisdictions by both public and private payors has led to a significant increase in the volume of therapeutic changes for patients. Pharmacists are well positioned to ensure effective and safe transitions, however there is a significant unmet need for objective and subjective clinical guidance around therapy as well disease state monitoring in RA that facilitates best practices throughout the patient journey. OBJECTIVE: In this paper we aim to create a consensus derived monitoring algorithm for pharmacists to facilitate best practices throughout therapeutic transitions from originator biologic to other originator biologics, biosimilars, and Janus kinase inhibitors in RA. METHODS: The Nominal Group Technique (NGT) was used to understand if consensus could be found among the participants. Clinically relevant questions were developed to capture solutions to the identified unmet need. The faculty considered the questions as individuals, and privately generated answers/ideas. After discussion and consideration, the participants ranked the ideas and established a consensus. RESULTS: Based on the outcome of the consensus discussions, an algorithm was created to help guide pharmacists through therapeutic transitions in RA. The tool covers important topics such as pre-transition considerations, avoiding the nocebo effect for biosimilars, specific considerations for each drug or class, monitoring efficacy, and when to refer. CONCLUSIONS: New classes of anti-rheumatic drugs including JAKi, along with the introduction of biosimilars are presenting more opportunity for therapeutic changes and monitoring in patients with RA. We hope our evidence-based consensus derived guidance tool will assist frontline pharmacists in supporting their patients to a successful therapeutic transition in RA.
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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 | 0.098 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".