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Record W2091421418 · doi:10.1164/rccm.200708-1217pp

Airway Smooth Muscle in Bronchial Tone, Inflammation, and Remodeling

2007· article· en· W2091421418 on OpenAlexfundno aff
Reynold A. Panettieri, Michael I. Kotlikoff, William T. Gerthoffer, Marc B. Hershenson, Prescott G. Woodruff, Ian P. Hall, Susan Banks‐Schlegel

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsnot available
FundersSchool of Medicine, Indiana UniversityUniversity of California, San FranciscoNational Heart, Lung, and Blood InstituteMcGill UniversitySchool of Medicine, Boston UniversityUniversity of PennsylvaniaNational Institutes of HealthWayne State UniversityU.S. Department of Health and Human Services
KeywordsMedicineAirwayInflammationCystic fibrosisAirway hyperresponsivenessAsthmaImmunologyLungNeuroscienceFibrosisPathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

Airway smooth muscle (ASM) plays a pivotal role in modulating bronchomotor tone but also orchestrates and perpetuates airway inflammation and remodeling. Despite substantial research, there remain important unanswered questions. In 2006, the National Heart, Lung, and Blood Institute sponsored a workshop to define new directions in ASM biology. Important questions concerning the key functions of ASM include the following: Does developmental dysregulation of ASM function promote airway disease, what key signaling pathways in ASM evoke airway hyperresponsiveness in vivo, do alterations in ASM mass affect excitation-contraction coupling, and can ASM modulate airway inflammation and remodeling in a physiologically relevant manner? This workshop identified critical issues in ASM biology to delineate areas for scientific investigation in the identification of new therapeutic and diagnostic approaches in asthma, chronic obstructive pulmonary disease, and cystic 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 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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.395
Teacher spread0.364 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations85
Published2007
Admission routes1
Has abstractyes

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