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Record W4387157834 · doi:10.1164/rccm.202306-1113st

Treatment of Systemic Sclerosis–associated Interstitial Lung Disease: Evidence-based Recommendations. An Official American Thoracic Society Clinical Practice Guideline

2023· article· en· W4387157834 on OpenAlexfundno aff
Ganesh Raghu, Sydney B. Montesi, Richard M. Silver, Tanzib Hossain, Madalina Macrea, Derrick Herman, Hayley Barnes, Ayodeji Adegunsoye, Arata Azuma, Leland W.K. Chung, Gregory C. Gardner, Kristin B. Highland, Marie Hudson, Robert J. Kaner, Martin Kolb, Mary Beth Scholand, Virginia Steen, Carey C. Thomson, Elizabeth R. Volkmann, Fredrick M. Wigley, Dee Burlile, Karen Kemper, Shandra L. Knight, Marya Ghazipura

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsnot available
FundersSchool of Medicine, Stanford UniversitySchool of Medicine, New York UniversityNational Institutes of HealthMonash UniversityBayer HealthCareNational Heart, Lung, and Blood InstituteAcceleronUniversity of South CarolinaUniversity of WashingtonUniversity of ChicagoOhio State UniversityU.S. Department of Veterans AffairsAlexion PharmaceuticalsYork UniversityBiogenGenentechVeracyteUnited Therapeutics CorporationMassachusetts General HospitalGlaxoSmithKlineCSL BehringBristol-Myers SquibbU.S. Department of DefenseEli Lilly and CompanyAstraZenecaPulmonary Fibrosis FoundationAmerican Thoracic SocietyBoehringer Ingelheim JapanFibroGenDepartment of Laboratory Medicine and Pathology, University of WashingtonGilead SciencesDivision of Rheumatology and Immunology, Medical University of South CarolinaNational Scleroderma Foundation
KeywordsMedicineNintedanibGuidelineRituximabIntensive care medicineInterstitial lung diseaseTocilizumabEvidence-based practiceInternal medicineIdiopathic pulmonary fibrosisPhysical therapyDiseaseAlternative medicinePathologyLung

Abstract

fetched live from OpenAlex

Abstract Background Interstitial lung disease (ILD) is a significant cause of morbidity and mortality in patients with systemic sclerosis (SSc). To date, clinical practice guidelines regarding treatment for patients with SSc-ILD are primarily consensus based. Methods An international expert guideline committee composed of 24 individuals with expertise in rheumatology, SSc, pulmonology, ILD, or methodology, and with personal experience with SSc-ILD, discussed systematic reviews of the published evidence assessed using the Grading of Recommendations, Assessment, Development, and Evaluation approach. Predetermined conflict-of-interest management strategies were applied, and recommendations were made for or against specific treatment interventions exclusively by the nonconflicted panelists. The confidence in effect estimates, importance of outcomes studied, balance of desirable and undesirable consequences of treatment, cost, feasibility, acceptability of the intervention, and implications for health equity were all considered in making the recommendations. This was in accordance with the American Thoracic Society guideline development process, which is in compliance with the Institute of Medicine standards for trustworthy guidelines. Results For treatment of patients with SSc-ILD, the committee: 1) recommends the use of mycophenolate; 2) recommends further research into the safety and efficacy of (a) pirfenidone and (b) the combination of pirfenidone plus mycophenolate; and 3) suggests the use of (a) cyclophosphamide, (b) rituximab, (c) tocilizumab, (d) nintedanib, and (e) the combination of nintedanib plus mycophenolate. Conclusions The recommendations herein provide an evidence-based clinical practice guideline for the treatment of patients with SSc-ILD and are intended to serve as the basis for informed and shared decision making by clinicians and patients.

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)0.014
metaresearch head score (Gemma)0.031
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: Methods · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0080.006
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0070.002
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0050.003

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 head0.093
GPT teacher head0.447
Teacher spread0.354 · 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
GenreMethods

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

Citations160
Published2023
Admission routes1
Has abstractyes

Explore more

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