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Record W2135376650 · doi:10.1164/rccm.201303-0522ws

Cell Therapy for Lung Diseases. Report from an NIH–NHLBI Workshop, November 13–14, 2012

2013· review· en· W2135376650 on OpenAlexaff
Michael A. Matthay, Piero Anversa, Jahar Bhattacharya, Bruce K. Burnett, Harold A. Chapman, Joshua M. Hare, Derek J. Hei, Andrew M. Hoffman, Stella Kourembanas, David H. McKenna, Luis A. Ortiz, Harald C. Ott, William E. Tente, Bernard Thébaud, Bruce C. Trapnell, Daniel J. Weiss, Jason X.‐J. Yuan, Carol J. Blaisdell

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2013
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Ottawa
FundersNational Heart, Lung, and Blood InstituteNational Institutes of Health
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

The National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health convened the Cell Therapy for Lung Disease Working Group on November 13-14, 2012, to review and formulate recommendations for future research directions. The workshop brought together investigators studying basic mechanisms and the roles of cell therapy in preclinical models of lung injury and pulmonary vascular disease, with clinical trial experts in cell therapy for cardiovascular diseases and experts from the NHLBI Production Assistance for Cell Therapy program. The purpose of the workshop was to discuss the current status of basic investigations in lung cell therapy, to identify some of the scientific gaps in current knowledge regarding the potential roles and mechanisms of cell therapy in the treatment of lung diseases, and to develop recommendations to the NHLBI and the research community on scientific priorities and practical steps that would lead to first-in-human trials of lung cell therapy.

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)0.002
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.003

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 head0.041
GPT teacher head0.385
Teacher spread0.345 · 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
GenreReview

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

Citations31
Published2013
Admission routes1
Has abstractyes

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