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Record W4388525448 · doi:10.1038/s41584-023-01045-w

International Guideline for Idiopathic Inflammatory Myopathy-Associated Cancer Screening: an International Myositis Assessment and Clinical Studies Group (IMACS) initiative

2023· review· en· W4388525448 on OpenAlexaff
Alexander Oldroyd, Jeffrey P. Callen, Hector Chinoy, Leland W.K. Chung, David Fiorentino, Patrick Gordon, Pedro Machado, Neil McHugh, Albert Selva-O’Callaghan, Jens Schmidt, Sarah Tansley, Ruth Ann Vleugels, Victoria P. Werth, Anthony A. Amato, Helena Andersson, Lilia Andrade-Ortega, Dana P. Ascherman, Olivier Benvéniste, Lorenzo Cavagna, Christina Charles-Shoeman, Benjamin F. Chong, Lisa Christopher‐Stine, Jennie T. Clarke, Emma J. Crosbie, Philip Crosbie, Sonye K. Danoff, Maryam Dastmalchi, Paul F. Dellaripa, Louise Pyndt Diederichsen, Mazen M. Dimachkie, Erik Ensrud, Floranne C. Ernste, D. Gareth Evans, Manabu Fujimoto, Ignacio García‐De La Torre, Abraham García-Kutzbach, Zoltán Griger, Latika Gupta, Marie Hudson, Florenzo Iannone, David Isenberg, Joseph L. Jorizzo, Helen Kurtz, Masataka Kuwana, Vidya Limaye, Ingrid E. Lundberg, Andrew L. Mammen, H. Mann, Frank Mastaglia, Lorna McWilliams, Christopher A. Mecoli, Federica Meloni, Frederick W. Miller, Siamak Moghadam‐Kia, Sergey Moiseev, Yoshinao Muro, Melinda Nagy‐Vincze, Clive Nayler, Merrilee Needham, Ichizo Nishino, Chester V. Oddis, Julie J. Paik, Joost Raaphorst, Lisa G. Rider, Jorge Rojas‐Serrano, Lesley Ann Saketkoo, Adam Schiffenbauer, Samuel Katsuyuki Shinjo, Vineeta Shobha, Yeong‐Wook Song, Tania Tillett, Yves Troyanov, Anneke J. van der Kooi, Mónica Vázquez-Del Mercado, Jiří Vencovský, Qian Wang, Steven R. Ytterberg, Rohit Aggarwal

Bibliographic record

VenueNature Reviews Rheumatology · 2023
Typereview
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de MontréalMcGill UniversityJewish General Hospital
FundersSpark TherapeuticsManchester Biomedical Research CentreSanofi GenzymeGrifolsMitsubishi Tanabe Pharma CorporationAstellas PharmaPrinses Beatrix SpierfondsUniversity College LondonDepartment of Health and Social CareGenentechAmicus TherapeuticsHorizon TherapeuticsMyositis AssociationEMD SeronoSarepta TherapeuticsArgenxNational Institute of Arthritis and Musculoskeletal and Skin DiseasesSanofiNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchHealth~HollandNational Institutes of HealthRegeneron PharmaceuticalsAlexion PharmaceuticalsKisseiAlnylam PharmaceuticalsGilead SciencesCelgenePfizerBiogenChugai PharmaceuticalConsejo Estatal de Ciencia y Tecnología de JaliscoGlaxoSmithKlineAmgenNational Institute for Health and Care ResearchCSL BehringAstraZenecaEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineGuidelineMyositisMyopathyInflammatory myopathyCancerPhysical therapyInternal medicineIntensive care medicinePathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.003

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 head0.166
GPT teacher head0.524
Teacher spread0.358 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations139
Published2023
Admission routes1
Has abstractno

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