Real World Data in Adaptive Biomedical Innovation: A Framework for Generating Evidence Fit for Decision‐Making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Analyses of healthcare databases (claims, electronic health records [EHRs]) are useful supplements to clinical trials for generating evidence on the effectiveness, harm, use, and value of medical products in routine care. A constant stream of data from the routine operation of modern healthcare systems, which can be analyzed in rapid cycles, enables incremental evidence development to support accelerated and appropriate access to innovative medicines. Evidentiary needs by regulators, Health Technology Assessment, payers, clinicians, and patients after marketing authorization comprise (1) monitoring of medication performance in routine care, including the materialized effectiveness, harm, and value; (2) identifying new patient strata with added value or unacceptable harms; and (3) monitoring targeted utilization. Adaptive biomedical innovation (ABI) with rapid cycle database analytics is successfully enabled if evidence is meaningful, valid, expedited, and transparent. These principles will bring rigor and credibility to current efforts to increase research efficiency while upholding evidentiary standards required for effective decision-making in healthcare.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it