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Record W2805191488 · doi:10.3899/jrheum.180007

Cancer-associated Dermatomyositis: Does the PD-1 Checkpoint Pathway Play a Role?

2018· letter· en· W2805191488 on OpenAlexvenueaboutno aff
Moisés Labrador‐Horrillo, Albert Selva-O’Callaghan

Bibliographic record

VenueThe Journal of Rheumatology · 2018
Typeletter
Languageen
FieldMedicine
TopicInflammatory Myopathies and Dermatomyositis
Canadian institutionsnot available
FundersEuropean Regional Development FundInstituto de Salud Carlos III
KeywordsMedicineDermatomyositisMalignancyCancerOccultDiseaseMyositisInternal medicineOncologyImmunologyPathology

Abstract

fetched live from OpenAlex

Cancer-associated myositis represents a unique opportunity to study the complex relationship between cancer and autoimmunity. It is well recognized that dermatomyositis (DM) is the inflammatory myopathy most often associated with cancer. In clinical practice, nearly one-third of patients with DM present with associated malignant disease, and the remainder may harbor an occult cancer that could develop in the near future, or never appear1,2. The immune system likely has a role in determining which of these outcomes will ensue. Management of a patient with DM is difficult, because an occult malignancy may not always be detected by the available screening tools. One explanation for the uncertainty regarding the development of malignant disease in these patients is the concept of 3 sequential phases in the relationship between cancer and the immune system: elimination, equilibrium, and escape3. In this framework, patients with cancer-associated DM can be viewed in different ways. First, they can be seen as DM patients with concurrent malignant disease, which would indicate that elimination of the malignancy by the immune system has failed. Second, the patients could have an occult malignancy, but the immune system maintains a tight balance to prevent it from full development to cancer. This is the equilibrium phase, which can last for decades and helps explain the higher incidence of cancer in patients with DM even years after the … Address correspondence to Dr. M. Labrador-Horrillo, Vall d’Hebron General Hospital, Internal Medicine, Passeig Vall d’Hebron, 119-129 Barcelona, 08035 Spain. E-mail: mlabrador{at}vhebron.net.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.004
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2018
Admission routes2
Has abstractyes

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