Bridging Stem Cell Research and Medicine: a Learning Health System
Bibliographic record
Abstract
Stem cells may not systematically obey traditional Phase I-IV clinical translation models. In response, various actors have suggested that stem cell-based medical innovation models could catalyze translation instead. Accordingly, calls were made to adopt more permissive approaches to stem cell translation. Yet, the Phase I-IV paradigm remains the standard within the scientific community. This article seeks to advance the stalemated discussions by proposing an alternative model for consideration. In it, we argue that a stem cell-based learning health system may be able to reconcile these two models. Centered on the acceleration of evidence and knowledge flow, a stem cell-based learning health system could maximize patient retention and data follow-up, thereby promoting inclusive system learning and improvement.
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 | 0.067 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.054 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".