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Record W2026447430 · doi:10.5966/sctm.2014-0122

Proceedings: International Regulatory Considerations on Development Pathways for Cell Therapies

2014· article· en· W2026447430 on OpenAlexaff
Ellen G. Feigal, Katherine Tsokas, Sowmya Viswanathan, Jiwen Zhang, Catherine Priest, Jonathan Pearce, Natalie Mount

Bibliographic record

VenueStem Cells Translational Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsComputational biologyRegulatory scienceBiologyMedicinePathology

Abstract

fetched live from OpenAlex

Regenerative medicine is a rapidly evolving field that faces novel scientific and regulatory challenges. In September 2013, the International Workshop on Regulatory Pathways for Cell Therapies was convened to discuss the nature of these challenges and potential solutions and to highlight opportunities for potential convergence between different regulatory bodies that might assist the field's development. The workshop discussions generated potentially actionable steps in five main areas that could mitigate cell therapy development pathway risk and accelerate moving promising therapies to patients. These included the need for convergence of regulatory guidelines on donor eligibility and suitability of lines for use in clinical trials and subsequent commercialization for cell therapies to move forward on a global basis; the need to challenge and encourage investigators in the regenerative medicine field to share information and provide examples of comparability studies related to master cell banks; the need for convergence of guidelines across regulatory jurisdictions on requirements for tumorigenicity studies, based on particular cell types and on biodistribution studies; the need to increase transparency in sharing clinical trial information more broadly and disseminating results more rapidly; and the need to establish a forum for sharing the experiences of various approaches being developed to expedite regulatory approvals and access for patients to innovative cell and regenerative therapies in the different regulatory jurisdictions and to assess their potential strengths and weaknesses.

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 imitation

Not 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.

metaresearch head score (Codex)0.061
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.061
Threshold uncertainty score0.325

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.072
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0020.001
Science and technology studies0.0100.008
Scholarly communication0.0220.010
Open science0.0070.010
Research integrity0.0520.047
Insufficient payload (model declined to judge)0.0190.007

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.

Opus teacher head0.042
GPT teacher head0.270
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations25
Published2014
Admission routes1
Has abstractyes

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