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Record W4308368681 · doi:10.1002/acr.25056

Patient and Physician Global Assessments of Disease Status in Systemic Sclerosis

2022· review· en· W4308368681 on OpenAlexaff
Laura Ross, Mandana Nikpour, Julie D’Aoust, Dinesh Khanna, Peter A. Merkel, John D Pauling, Murray Baron

Bibliographic record

VenueArthritis Care & Research · 2022
Typereview
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsJewish General HospitalMcGill UniversityOttawa HospitalUniversity of Ottawa
FundersNational Health and Medical Research CouncilMedical Research CouncilPfizerArthritis AustraliaEli Lilly and Company
KeywordsMedicineStandardizationRandomized controlled trialDiseasePhysical therapyClinical trialGlobal healthMEDLINEIntensive care medicineInternal medicinePathologyComputer sciencePublic health

Abstract

fetched live from OpenAlex

Global assessments of disease by both patients and physicians are widely used in clinical studies of systemic sclerosis (SSc). They are commonly secondary end points in randomized controlled trials (RCTs) and are considered important items in composite measures of treatment response. A comprehensive literature review was conducted of the formats, wording, and clinimetric properties of the patient global assessment of disease status (PtGA) and physician global assessment of disease status (PhGA) used in RCTs of SSc. Marked heterogeneity was found in the wording and measurement scales of the global assessments applied in RCTs. These instruments were not developed using rigorous methodology and have not been fully validated. There is a pressing need for standardization and validation of patient and physician global assessment tools in SSc to enable universal application of these measures across RCTs in SSc.

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)0.006
metaresearch head score (Gemma)0.009
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: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0060.005
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.111
GPT teacher head0.416
Teacher spread0.305 · 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
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

Citations17
Published2022
Admission routes1
Has abstractyes

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