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Record W2742563138 · doi:10.1002/sctm.17-0047

Proceedings: Regenerative Medicine for Lung Diseases: A CIRM Workshop Report

2017· article· en· W2742563138 on OpenAlexfundno aff
Lisa C. Kadyk, Natalie DeWitt, Brigitte N. Gomperts

Bibliographic record

VenueStem Cells Translational Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsnot available
FundersWeizmann Institute of ScienceCedars-Sinai Medical CenterOttawa Hospital Research InstituteCalifornia Institute for Regenerative Medicine
KeywordsRegenerative medicineStem cellMedicineLungIntensive care medicineBioinformaticsPathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

The mission of the California Institute of Regenerative Medicine (CIRM) is to accelerate treatments to patients with unmet medical needs. In September 2016, CIRM sponsored a workshop held at the University of California, Los Angeles, to discuss regenerative medicine approaches for treatment of lung diseases and to identify the challenges remaining for advancing such treatments to the clinic and market approval. Workshop participants discussed current preclinical and clinical approaches to regenerative medicine in the lung, as well as the biology of lung stem cells and the role of stem cells in the etiology of various lung diseases. The outcome of this effort was the recognition that whereas transient cell delivery approaches are leading the way in the clinic, recent advances in the understanding of lung stem cell biology, in vitro and in vivo disease modeling, gene editing and replacement methods, and cell engraftment approaches raise the prospect of developing cures for some lung diseases in the foreseeable future. In addition, advances in in vitro modeling using lung organoids and "lung on a chip" technology are setting the stage for high quality small molecule drug screening to develop treatments for lung diseases with complex biology. Stem Cells Translational Medicine 2017;6:1823-1828.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.330
Teacher spread0.292 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations8
Published2017
Admission routes1
Has abstractyes

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