3rd Colombian educational workshop on regulatory assessment of biosimilars 2019 – Report
Bibliographic record
Abstract
Introduction: Biosimilars have the potential to improve access to medicines for many across the globe. However, work is required to ensure adequate regulation, pharmacovigilance and education about biosimilars. Colombia implemented biosimilars regulation in 2017 and a 3rd Colombian Educational Workshop was organized by GaBI and the Instituto Nacional de Vigilancia de Medicamentos y Alimentos (INVIMA) in 2019 to follow up on progress and provide a forum for further discussion. Methods: The 3rd Colombian Educational Workshop on Regulatory Assessment was held in Bogotá, Colombia on 30 April 2019. The format included expert speaker presentations, a panel discussion, Q&A sessions and case study workgroup discussions. Participants included regulators, clinicians, pharmacists, academics and healthcare professionals from Colombia who are involved in biological/ biosimilar medicines evaluation, and expert speakers from Canada, Europe and the US. Results: Presentations and topics of discussion included the current status of biosimilars regulation in Colombia, how to carry out a quality assessment of a biological/biosimilar, pharmacological and clinical studies, and extrapolation of indications. Conclusion: The meeting helped to clarify many regulatory concepts and concerns, and highlighted Colombia’s initial successes since the implementation of its regulatory guidelines. In addition, the meeting acted as a forum to exchange knowledge on best practice, and to discuss pharmacovigilance and the future plans for education regarding biosimilars in Colombia. Several key action points were concluded following the discussions.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.012 |
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".