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Record W2124702264 · doi:10.1002/acr.20187

Psychological health and well‐being in systemic sclerosis: State of the science and consensus research agenda

2010· review· en· W2124702264 on OpenAlexafffund
Brett D. Thombs, Wim van Lankveld, Marielle Bassel, Murray Baron, Robert Buzza, Shirley Haslam, Jennifer A. Haythornthwaite, Marie Hudson, Lisa R. Jewett, Ruby Knafo, Linda Kwakkenbos, Vanessa L. Malcarne, Katherine Milette, Sarosh J. Motivala, Evan G. Newton, Warren R. Nielson, Marion Pacy, Ilya Razykov, Orit Schieir, Suzanne Taillefer, Maureen Worron‐Sauvé

Bibliographic record

VenueArthritis Care & Research · 2010
Typereview
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsUniversity of TorontoSt Joseph's Health CareWestern UniversityMultiple Sclerosis Society of CanadaMcGill UniversityJewish General Hospital
FundersNational Center for Complementary and Integrative HealthCanadian Institutes of Health Research
KeywordsPsychological scienceState (computer science)Multiple sclerosisPolitical sciencePsychologyMedicineSocial psychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

Introduction Systemic sclerosis (SSc; scleroderma) is a multisystem disorder characterized by disturbance in fibroblast function, microvascular disease, and immune system activation, culminating in fibrosis of the skin and internal organs (1,2). SSc is associated with extensive morbidity, including disfiguring skin thickening, finger ulcers, joint contractures, pulmonary hypertension, interstitial lung disease, chronic diarrhea, and renal failure (1,2). The rate of disease onset is highest between 30 and 50 years of age, with the risk for women being 4 to 5 times higher than for men (3,4). Median survival time from diagnosis is 11 years, and patients are 3.7 times more likely to die within 10 years of diagnosis (44.9% mortality) than age-, sex-, and race-matched individuals without SSc (12.0% mortality) (3).

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)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.003
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 head0.229
GPT teacher head0.474
Teacher spread0.244 · 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.

Study designOther design
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

Citations103
Published2010
Admission routes2
Has abstractyes

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