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Record W2158781546 · doi:10.3899/jrheum.081284

The Localized Scleroderma Skin Severity Index and Physician Global Assessment of Disease Activity: A Work in Progress Toward Development of Localized Scleroderma Outcome Measures

2009· article· en· W2158781546 on OpenAlexaffvenue
Thaschawee Arkachaisri, Soamarat Vilaiyuk, Suzanne C. Li, Kathleen M. O’Neil, Elena Pope, Gloria C. Higgins, Marilynn Punaro, Egla Rabinovich, Margalit Rosenkranz, Daniel Kietz, Paul Peter Rosen, Steven J. Spalding, Teresa Hennon, Kathryn S. Torok, Elaine Cassidy, Thomas A. Medsger

Bibliographic record

VenueThe Journal of Rheumatology · 2009
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesCardiff UniversityUniversity of Pittsburgh
KeywordsMedicineIntra-rater reliabilityIntraclass correlationDermatology Life Quality IndexInter-rater reliabilityQuality of life (healthcare)ErythemaPhysical therapySeverity of illnessReliability (semiconductor)Scleroderma (fungus)SurgeryInternal medicineDiseaseConfidence intervalPsychometricsPathologyRating scalePsychology

Abstract

fetched live from OpenAlex

OBJECTIVE: To develop and evaluate a Localized Scleroderma (LS) Skin Severity Index (LoSSI) and global assessments' clinimetric property and effect on quality of life (QOL). METHODS: A 3-phase study was conducted. The first phase involved 15 patients with LS and 14 examiners who assessed LoSSI [surface area (SA), erythema (ER), skin thickness (ST), and new lesion/extension (N/E)] twice for inter/intrarater reliability. Patient global assessment of disease severity (PtGA-S) and Children's Dermatology Life Quality Index (CDLQI) were collected for intrarater reliability evaluation. The second phase was aimed to develop clinical determinants for physician global assessment of disease activity (PhysGA-A) and to assess its content validity. The third phase involved 2 examiners assessing LoSSI and PhysGA-A on 27 patients. Effect of training on improving reliability/validity and sensitivity to change of the LoSSI and PhysGA-A was determined. RESULTS: Interrater reliability was excellent for ER [intraclass correlation coefficient (ICC) 0.71], ST (ICC 0.70), LoSSI (ICC 0.80), and PhysGA-A (ICC 0.90) but poor for SA (ICC 0.35); thus, LoSSI was modified to mLoSSI. Examiners' experience did not affect the scores, but training/practice improved reliability. Intrarater reliability was excellent for ER, ST, and LoSSI (Spearman's rho = 0.71-0.89) and moderate for SA. PtGA-S and CDLQI showed good intrarater agreement (ICC 0.63 and 0.80). mLoSSI correlated moderately with PhysGA-A and PtGA-S. Both mLoSSI and PhysGA-A were sensitive to change following therapy. CONCLUSION: mLoSSI and PhysGA-A are reliable and valid tools for assessing LS disease severity and show high sensitivity to detect change over time. These tools are feasible for use in routine clinical practice. They should be considered for inclusion in a core set of LS outcome measures for 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)0.010
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.316
Teacher spread0.285 · 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 designObservational
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

Citations181
Published2009
Admission routes2
Has abstractyes

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