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POS1069 SYSTEMATIC LITERATURE REVIEW INFORMING THE EULAR POINTS TO CONSIDER TASK FORCE ON THE INITIATION OF TARGETED THERAPIES IN PATIENTS WITH INFLAMMATORY ARTHRITIDES AND A HISTORY OF CANCER

2023· article· en· W4379650075 on OpenAlexaboutno aff
E. Sebbag, J. Molina Collada, Kim Lauper, Daniel Aletaha, Johan Askling, Karolina Benesova, Heidi Bertheussen, Samuel Bitoun, Ertuğrul Çağrı Bölek, Gerd R Burmester, Helena Canhão, Katerina Chatzidionysiou, Jeffrey R. Curtis, F.X. Danlos, V. Guimaraes, Merete Lund Hetland, Florenzo Iannone, Marie Kostine, Tue Wenzel Kragstrup, Tore K Kvien, Anne C. Regierer, Hendrik Schulze‐Koops, Lucía Silva-Fernández, Zoltán Szekanecz, Maya H Buch, Axel Finckh, Jacques‐Eric Gottenberg

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsnot available
FundersDebreceni EgyetemHôpitaux Universitaires de GenèveDiakonhjemmetHacettepe ÜniversitesiUniversita degli Studi di Bari Aldo MoroUniversität WienCelltrionAarhus UniversitetMedizinische Universität WienAarhus UniversitetshospitalInstitut Gustave-RoussyGilead SciencesKarolinska InstitutetRigshospitaletAmgenPfizerEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineInternal medicineOncologyTask forceCancerInflammatory arthritisMEDLINEHazard ratioPhysical therapyRheumatoid arthritisConfidence interval

Abstract

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<h3>Background</h3> Potential associations between targeted therapies in patients with an inflammatory arthritis (IA) and malignancy are a frequent concern in daily rheumatology practice. No specific framework has been proposed to evaluate the benefit/risk balance of initiating or reinitiating a targeted therapy (bDMARDs/tsDMARDs) in patients with IA and a history of cancer. <h3>Objectives</h3> To perform a systematic literature review (SLR) to inform the task force formulating. the EULAR Points to Consider on the initiation of targeted therapies in patients. with IA and a history of cancer. <h3>Methods</h3> Specific research points were defined with the task force before formulating. the research questions with a librarian under supervision of two methodologists. The task force agreed to focus the SLR on clinical data in patients treated with any targeted therapy for an inflammatory or autoimmune rheumatic or skin or bowel disease. All studies up to. the 15th July 2022 were searched through Pubmed and Embase. Inclusion criteria required studies reporting on the initiation of a targeted therapy in patients with history of cancer, a control group. of patients treated with a conventional DMARDs or healthy controls, and report of a relative risk measure (e.g. Hazard Ratio (HR)) of cancer recurrence between groups. Two reviewers independently performed standardized article selection, data extraction, synthesis, and risk of bias assessment. The quality of the studies was graded according to the Newcastle-Ottawa quality assessment scale. <h3>Results</h3> A total of 1555 publications were identified of which 79 articles fulfilled inclusion criteria, including. 13 published articles and 1 EULAR abstract. All studies were high quality observational data from cohorts or registries, representing 4522 patients (13030 patient-years). Most of the patients included were treated for rheumatoid arthritis. The previous cancer was a solid cancer for more than 90% of the patients. The targeted therapy evaluated was a TNF inhibitor in all the studies, and 4 studies evaluated rituximab as well. The overall HR of cancer recurrence was 1.02 (0.83-1.26) in patients treated with a targeted therapy compared to those treated with a conventional DMARD (Figure 1). In patients treated with. a TNF-inhibitor, the HR was 1.01 (0.86-1.18). In patients treated with rituximab, the HR. was 1.10 (0.72-1.67). In subgroup analyses, no difference in cancer recurrence was observed if. the targeted therapy was initiated before or after 5 years since the diagnosis of the initial cancer; no difference in cancer recurrence was observed depending on the initial cancer type. <h3>Conclusion</h3> The SLR informing EULAR PTC show that overall, the targeted therapies and clinical context covered by the included studies were not associated with an increased risk of cancer recurrence when compared with conventional synthetic DMARDs. This SLR also shows the lack of data for other targeted therapies, for other clinical contexts, and for other conditions than RA. <h3>REFERENCES:</h3> NIL. <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> Eden Sebbag: None declared, Juan Molina Collada: None declared, Kim Lauper: None declared, Daniel Aletaha: None declared, Johan Askling: None declared, Karolina Benesova: None declared, Heidi Bertheussen: None declared, Samuel Bitoun: None declared, Ertugrul Cagri Bolek: None declared, Gerd Rüdiger Burmester: None declared, Helena Canhão: None declared, Katerina Chatzidionysiou: None declared, Jeffrey Curtis: None declared, François-Xavier Danlos: None declared, vera guimaraes: None declared, Merete Lund Hetland: None declared, Florenzo Iannone: None declared, Marie Kostine: None declared, Tue Wenzel Kragstrup Speakers bureau: Pfizer, Bristol-Myers Squibb, Eli Lilly, Novartis, UCB, and Abbvie, Consultant of: Bristol-Myers Squibb, UCB, Gilead, and Eli-Lilly, Tore K. Kvien Speakers bureau: Grünenthal, Sandoz, UCB, Consultant of: AbbVie, Amgen, Celltrion, Gilead, Novartis, Pfizer, Sandoz, UCB, Grant/research support from: AbbVie, Amgen, BMS, Galapagos, Novartis, Pfizer, UCB, Anne Regierer: None declared, Hendrik Schulze-Koops: None declared, Lucía Silva-Fernández Speakers bureau: Novartis, MSD, Sanofi, Janssen, Pfizer, Consultant of: Lilly, BMS, Abbvie, Novartis, Janssen., Zoltan Szekanecz: None declared, Maya H Buch: None declared, Axel Finckh Speakers bureau: AbbVie, BMS, Pfizer, Eli-Lilly, Sandoz, Consultant of: AbbVie, Novartis, Pfizer, MSD, Lilly, Grant/research support from: AbbVie, BMS, Galapagos, Lilly, Pfize, Jacques-Eric Gottenberg Consultant of: Abbvie, BMS, Galapagos, Gilead, Jannsen, Lilly, Roche Chugai, Sanofi, Pfizer, UCB, Grant/research support from: Abbvie, BMS, Pfizer.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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 teacher head, 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".

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Citations6
Published2023
Admission routes1
Has abstractyes

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