MétaCan
Menu
Back to cohort
Record W2918666649 · doi:10.1136/bcr-2018-227995

Immune checkpoint inhibitor-induced rheumatoid arthritis: insights into an increasingly common aetiology of polyarthritis

2019· article· en· W2918666649 on OpenAlexaff
Timothy S.H. Kwok, Mary Bell

Bibliographic record

VenueBMJ Case Reports · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsNivolumabMedicineRheumatoid arthritisPolyarthritisAdverse effectImmunotherapyImmune systemCancerOncologyArthritisAdjuvantInternal medicineImmunology

Abstract

fetched live from OpenAlex

Nivolumab is an immune checkpoint inhibitor that is used in the treatment of a variety of cancers in the adjuvant or metastatic setting. Adverse effects include non-specific activation of T cells, leading to immune-related adverse events in downstream organs. We present a case of a 36-year-old man with unresectable oropharyngeal squamous cell carcinoma who developed nivolumab-induced rheumatoid arthritis. As immune checkpoint inhibitor use is becoming widespread in the medical oncology domain, the purpose of this case report is to increase awareness of an increasingly common cause of rheumatic disease and to alert clinicians to consider immunotherapy in their differential diagnosis of polyarthritis. This case also highlights the importance of working in an interdisciplinary manner to enhance cancer care for the patient as well as to increase awareness of the potential adverse effects of immunotherapy in patients with cancer.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.014
GPT teacher head0.286
Teacher spread0.272 · 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 designCase report
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

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueBMJ Case ReportsSame topicCancer Immunotherapy and BiomarkersFrench-language works237,207