Biologic Therapies: From Complexity to Clinical Practice in a Changing Environment
Bibliographic record
Abstract
This symposium provided an opportunity for global experts to discuss the challenges posed by the introduction of biosimilars. The impact of the manufacturing process on clinical outcomes, maintaining treatment responses over the long term, and issues surrounding patient management in a changing environment were addressed. The symposium was opened by Prof Panaccione describing the evolution of inflammatory bowel disease (IBD) treatment in the last 20 years and how biologics have improved outcomes. Prof D’Haens provided an explanation of the complexity surrounding biologic drug development and the hurdles facing drug manufacturers when ensuring high quality and consistently performing products over time. Prof Panaccione discussed the clinical challenges in balancing the transition from induction to maintenance therapy in order to provide a clinically relevant and sustained response to therapy. He also discussed the evidence for long-term outcomes with adalimumab for IBD. Prof Feagan highlighted the issues faced by clinicians treating patients with biologics, including the ability to switch between biologics without loss of efficacy or impact on safety, and the need to consider interchangeability between biologic therapies and the potential risk and impact of immunogenicity.
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 | 0.050 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.023 | 0.026 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.013 | 0.031 |
| Insufficient payload (model declined to judge) | 0.009 | 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".