The Implementation of Novel Collaborative Structures for the Identification and Resolution of Barriers to Pluripotent Stem Cell Translation
Bibliographic record
Abstract
Increased global connectivity has catalyzed technological development in almost all industries, in part through the facilitation of novel collaborative structures. Notably, open innovation and crowd-sourcing-of expertise and/or funding-has tremendous potential to increase the efficiency with which biomedical ecosystems interact to deliver safe, efficacious and affordable therapies to patients. Consequently, such practices offer tremendous potential in advancing development of cellular therapies. In this vein, the CASMI Translational Stem Cell Consortium (CTSCC) was formed to unite global thought-leaders, producing academically rigorous and commercially practicable solutions to a range of challenges in pluripotent stem cell translation. Critically, the CTSCC research agenda is defined through continuous consultation with its international funding and research partners. Herein, initial findings for all research focus areas are presented to inform global product development strategies, and to stimulate continued industry interaction around biomanufacturing, strategic partnerships, standards, regulation and intellectual property and clinical adoption.
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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 | 0.036 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".