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Record W2940211539 · doi:10.1016/j.amjms.2019.02.014

The Fibrosis Across Organs Symposium: A Roadmap for Future Research Priorities

2019· article· en· W2940211539 on OpenAlexafffund
Jesse Roman, Teresa Barnes, Dolly Kervitsky, Gregory P. Cosgrove, Dennis E. Doherty, Andrew M. Tager, Luca Richeldi, Eric S. White, David A. Brenner, Lynn M. Schnapp, Tim D. Hewitson, Bodh I. Jugdutt, Timothy A. McKinsey, John D. Tosi, Stephen C. Crane, Kevin M. Brown

Bibliographic record

VenueThe American Journal of the Medical Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsUniversity of Alberta
FundersUniversity of Colorado DenverUniversity of Texas Health Science Center at San AntonioUniversity of California, San DiegoNational Institutes of HealthUniversity of AlbertaUniversity of California, San FranciscoVanderbilt University Medical CenterVanderbilt UniversityYale UniversityGilead SciencesMcMaster UniversityGenentechUniversity of PittsburghNational Institute of Diabetes and Digestive and Kidney DiseasesNational Jewish HealthJohns Hopkins UniversityHermansky-Pudlak Syndrome NetworkPulmonary Fibrosis FoundationCoalition for Pulmonary FibrosisUniversity of Southern CaliforniaNational Heart, Lung, and Blood InstituteAmerican Thoracic Society
KeywordsFibrosisMedicineIdiopathic pulmonary fibrosisPulmonary fibrosisPathologyBioinformaticsLungInternal medicineBiology

Abstract

fetched live from OpenAlex

The ability to develop discrete areas of tissue fibrosis or scarring represents a normal physiologic response to injury that is necessary for the maintenance of tissue integrity and normal organ function. In contrast, uncontrolled, dysregulated fibroproliferation and abnormal matrix deposition/remodeling results in tissue fibrosis and organ dysfunction, regardless of the initiating event.1 The latter process underlies a wide variety of clinically significant disorders ranging from idiopathic pulmonary fibrosis (IPF) and liver cirrhosis to kidney sclerosis and cutaneous fibrosis.

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)0.027
metaresearch head score (Gemma)0.025
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: Review · Consensus signal: Review
Teacher disagreement score0.056
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.025
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0050.004
Science and technology studies0.0030.004
Scholarly communication0.0080.017
Open science0.0040.008
Research integrity0.0120.016
Insufficient payload (model declined to judge)0.0560.016

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 head0.040
GPT teacher head0.386
Teacher spread0.347 · 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

Citations4
Published2019
Admission routes2
Has abstractyes

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