Recognizing that Evidence is Made, not Born
Bibliographic record
Abstract
Therapeutic product development, licensing and reimbursement may seem a well-oiled machine, but continuing high attrition rates, regulatory refusals, and patients' access issues suggest otherwise; despite serious efforts, gaps persist between stakeholders' stated evidence requirements and actual evidence supplied. Evidentiary deficiencies and/or human tendencies resulting in avoidable inefficiencies might be further reduced with fresh institutional cultures/mindsets, combined with a context-adaptable practices framework that integrates emerging innovations. Here, Structured Evidence Planning, Production, and Evaluation (SEPPE) posits that evidence be treated as something produced, much like other manufactured goods, for which "built-in quality" (i.e., "people" and "process") approaches have been successfully implemented globally. Incorporating proactive, iterative feedback-and-adjust loops involving key decision-makers at critical points could curtail avoidable evidence quality and decision hazards-pulling needed therapeutic products with high quality evidence of beneficial performance through to approvals. Critical for success, however, is dedicated, long-term commitment to systemic transformation.
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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 | 0.152 | 0.405 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.024 | 0.034 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.012 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".