Cancer-associated Dermatomyositis: Does the PD-1 Checkpoint Pathway Play a Role?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".