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Record W4416247829 · doi:10.3899/jrheum.2025-0696

Arthroplasty Joint Histology and Surgical Outcomes in Patients on Immune Checkpoint Inhibitors: A Single-Center Case Series

2025· article· en· W4416247829 on OpenAlexvenueno aff
Karmela K Chan, Nilasha Ghosh, Carlos A. Aude, Edward F. DiCarlo, Anne R. Bass

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsnot available
FundersRheumatology Research Foundation
KeywordsArthritisJoint arthroplastyInflammatory arthritisOsteoarthritisArthroplastyInflammationJoint replacementCancer

Abstract

fetched live from OpenAlex

Plain Language Summary This is a case series of 16 patients with cancer who were treated with immune checkpoint inhibitor (ICI) therapy and who underwent 23 joint replacements. Some of these patients were known to have inflammatory arthritis (joint inflammation) as a side effect of the ICI therapy, whereas others were thought to have worsening of established osteoarthritis (OA). We wanted to know whether the patients with OA had ICI-related joint inflammation that was simply not recognized before surgery. We also wanted to know whether patients who undergo joint replacements on ICI have more surgery complications than other people. What we found was that the people who were diagnosed with OA prior to surgery had OA on joint histology. Only the patients with known inflammatory arthritis from ICI had significant inflammation on joint histology. One patient required a revision of her hip joint replacement 3.7 years after surgery (while still on ICI) due to an inflammatory reaction to her joint replacement. We could not determine whether ICI played a role in that complication.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.012
GPT teacher head0.250
Teacher spread0.237 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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