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Record W2589893823 · doi:10.5301/jsrd.5000231

Standardization of the Modified Rodnan Skin Score for Use in Clinical Trials of Systemic Sclerosis

2017· article· en· W2589893823 on OpenAlexaff
Dinesh Khanna, Daniel E. Furst, Philip J. Clements, Yannick Allanore, Murray Baron, Oliver Distler, Ivan Foeldvari, Masataka Kuwana, Marco Matucci‐Cerinic, Maureen D. Mayes, Thomas A. Medsger, Peter A. Merkel, Janet Pope, James R. Seibold, Virginia Steen, Wendy Stevens, Christopher P. Denton

Bibliographic record

VenueJournal of Scleroderma and Related Disorders · 2017
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsWestern UniversityMcGill UniversityJewish General Hospital
FundersChugai PharmaceuticalGenentechNational Institutes of HealthMedacBristol-Myers SquibbCSL BehringSanofiServierAmgenPfizerNational Institute of Arthritis and Musculoskeletal and Skin DiseasesBiogenGlaxoSmithKline
KeywordsMedicineScleroderma (fungus)Clinical trialStandardizationMeasure (data warehouse)Progressive systemic sclerosisMedical physicsPhysical therapyInternal medicinePathologyComputer scienceData mining

Abstract

fetched live from OpenAlex

The modified Rodnan skin score (mRSS) is a measure of skin thickness and is used as a primary or secondary outcome measure in clinical trials of systemic sclerosis (scleroderma). This state-of-art review provides a historical perspective of the development of the mRSS, summarizes the performance of mRSS as an outcome measure, provides guidance on assessing mRSS, and makes recommendations for incorporation of the mRSS into clinical trials.

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)0.127
metaresearch head score (Gemma)0.211
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 score0.127
Threshold uncertainty score0.671

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1270.211
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0070.007
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0040.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.139
GPT teacher head0.367
Teacher spread0.229 · 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

Citations521
Published2017
Admission routes1
Has abstractyes

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