Health Canada/BIOTECanada Summit on regulatory and clinical topics related to subsequent entry biologics (biosimilars), Ottawa, Canada, 14 May 2012
Bibliographic record
Abstract
In May 2012, Health Canada and other participants held a National Summit on Subsequent Entry Biologics (SEBs). Health Canada released a guidance document in March 2010 describing policy positions and data requirements for approval of SEBs. While Health Canada and health agencies in other regulatory jurisdictions are aligned on many scientific principles related to biosimilar drugs, Health Canada's specific requirements may not be widely understood by many Canadian stakeholders. The Summit provided an opportunity for education and dialog among physicians who prescribe biologics, provincial payers, and industry on the following topics: preclinical and clinical comparability studies; manufacturing and other product differences; extrapolation of indications; substitution and interchangeability of SEBs with reference biologic drugs in clinical practice; payers' current perspective; pharmacovigilance and naming. It is anticipated that the consensus reached at this meeting will further educate Canadian healthcare professionals, provincial payers, and insurers about the appropriate use of SEBs, and may be of general interest to others internationally.
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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".