PANLAR consensus statement on biosimilars
Bibliographic record
Abstract
Biologics have improved the treatment of rheumatic diseases, resulting in better outcomes. However, their high cost limits access for many patients in both North America and Latin America. Following patent expiration for biologicals, the availability of biosimilars, which typically are less expensive due to lower development costs, provides additional treatment options for patients with rheumatic diseases. The availability of biosimilars in North American and Latin American countries is evolving, with differing regulations and clinical indications. The objective of the study was to present the consensus statement on biosimilars in rheumatology developed by Pan American League of Associations for Rheumatology (PANLAR). Using a modified Delphi process approach, the following topics were addressed: regulation, efficacy and safety, extrapolation of indications, interchangeability, automatic substitution, pharmacovigilance, risk management, naming, traceability, registries, economic aspects, and biomimics. Consensus was achieved when there was agreement among 80% or more of the panel members. Three Delphi rounds were conducted to reach consensus. Questionnaires were sent electronically to panel members and comments about each question were solicited. Eight recommendations were formulated regarding regulation, pharmacovigilance, risk management, naming, traceability, registries, economic aspects, and biomimics. The recommendations highlighted that, after receiving regulatory approval, pharmacovigilance is a fundamental strategy to ensure safety of all medications. Registries should be employed to monitor use of biosimilars and to identify potential adverse effects. The price of biosimilars should be significantly lower than that of reference products to enhance patient access. Biomimics are not biosimilars and, if they are to be marketed, they must first be evaluated and approved according to established regulatory pathways for novel biopharmaceuticals. • Biologics have improved the treatment of rheumatic diseases. • Their high cost limits access for many patients in both North America and Latin America. • Biosimilars typically are less expensive, providing additional treatment options for patients with rheumatic diseases. • PANLAR presents its consensus on biosimilars in rheumatology
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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 | 0.119 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.023 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".