Putting the substance in substantial evidence: an evidence-based approach to flexible drug regulation
Bibliographic record
Abstract
For new drugs or indications, substantial evidence of clinical effectiveness is required for market authorization. In most jurisdictions, substantial evidence is not explicitly defined. Health regulators exercise discretion and are increasingly tolerant of earlier or less mature evidence. To align with flexible evidentiary standards, we argue for the adoption of a principle and, context-based approach to the evidence threshold. Our approach aims to balance the potential benefits and harms of accelerated authorization, low therapeutic value, and safety, based on a value of information (VoI) framework. In our VoI framework, substantial evidence exists when the expected net health value of further research is less than or equal to zero. We operationalize this approach through two case examples that mirror real-time decision factors such as uncertainty, risk preferences and time inputs. As the evidentiary assessment landscape shifts towards flexibility, iterative and clearly defined approaches to risk assessment are warranted. Clarity will stimulate transparency and accountability for both stakeholders and regulators.
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 | 0.400 | 0.414 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.006 |
| Science and technology studies | 0.006 | 0.059 |
| Scholarly communication | 0.032 | 0.036 |
| Open science | 0.012 | 0.023 |
| Research integrity | 0.030 | 0.032 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".