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Record W4382797858 · doi:10.3899/jrheum.2023-0321

The Relationship Between Red Cell Distribution Width, Absolute Lymphocyte Count, and Rheumatoid Arthritis

2023· letter· en· W4382797858 on OpenAlexvenueno aff
Gang Wang, Zhichun Liu

Bibliographic record

VenueThe Journal of Rheumatology · 2023
Typeletter
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsnot available
FundersSecond Affiliated Hospital of Soochow UniversitySoochow University
KeywordsRheumatoid arthritisMedicineRed blood cell distribution widthMonocyteInflammationLymphocyteImmunologyArthritisInternal medicineDistribution (mathematics)

Abstract

fetched live from OpenAlex

We read with interest the recent publication by Lange and colleagues on the red cell distribution width (RDW) and absolute lymphocyte count (ALC) associated with biomarkers of inflammation and subsequent mortality in rheumatoid arthritis (RA).1 The authors concluded that RDW and ALC before disease-modifying antirheumatic drug (DMARD) therapy are associated with biomarkers of monocyte/macrophage inflammation and subsequent mortality.1

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)0.003
metaresearch head score (Gemma)0.025
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 score0.020
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0200.026
Insufficient payload (model declined to judge)0.0030.004

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 head0.024
GPT teacher head0.258
Teacher spread0.234 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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