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Record W2511948688 · doi:10.3899/jrheum.160386

Defining Low Disease Activity States in Psoriatic Arthritis Using Novel Composite Disease Instruments

2016· letter· en· W2511948688 on OpenAlexvenueno aff
Ennio Lubrano, Fabio Massimo Perrotta

Bibliographic record

VenueThe Journal of Rheumatology · 2016
Typeletter
Languageen
FieldMedicine
TopicSpondyloarthritis Studies and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVisual analogue scalePsoriatic arthritisPsoriasisArthritisDiseaseSeverity of illnessPhysical therapyDermatologyInternal medicine

Abstract

fetched live from OpenAlex

To the Editor: We read with great interest the recent article by Coates and Helliwell on another definition of low disease activity using the novel composite indices to define remission in patients with psoriatic arthritis (PsA)1. In 2010, the same authors developed a composite outcome measure as a target of treatment for patients with PsA2. Minimal disease activity (MDA) criteria are fulfilled when 5/7 of the following criteria were satisfied: tender joint count ≤ 1; swollen joint count ≤ 1; Psoriasis Area and Severity Index ≤ 1 or body surface area ≤ 3; patient pain visual analog scale (VAS) score ≤ 15 mm; patient global disease activity VAS score of ≤ 20 … Address correspondence to Dr. Lubrano; E-mail: enniolubrano{at}hotmail.com

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)0.004
metaresearch head score (Gemma)0.030
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0120.016
Insufficient payload (model declined to judge)0.0020.003

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 head0.015
GPT teacher head0.264
Teacher spread0.249 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

Explore more

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