Bibliographic record
Abstract
Samenvatting Melanoompatienten die een autoimmuunaandoening zoals vitiligo ontwikkelen, hebben over het algemeen een grotere kans op overleving. Bij deze patienten is de autoimmuniteit veelal een neveneffect van de specifieke immuuntherapie die gericht is tegen het melanoom. Vitiligo kan als neveneffect optreden, indien de therapie een immuunrespons opwekt die gericht is tegen antigenen op zowel melanoomcellen als normale melanocyten. Bij melanoompatienten met een lange overleving na immuuntherapie in afwezigheid van vitiligo, is de immuniteit veelal gericht tegen tumorgeassocieerde antigenen die niet op normale melanocyten voorkomen. Dit overzichtsartikel gaat in op de manier waarop vitiligo en het melanoom immunologisch gezien elkaars tegenpolen vormen, en hoe de pathogenese van vitiligo een remmend effect op het melanoom kan hebben. (Ned Tijdschr Oncol 2006;3:224-30) Summary Melanoma patients who develop autoimmune diseases such as vitiligo, generally achieve prolonged survival. In these patients autoimmunity is often a side effect of the immunotherapy that targets the melanoma cells. Vitiligo can occur as a side effect if the immune respons that is activated by the immunotherapy targets antigens that are expressed by both melanoma cells and normal melanocytes. In melanoma patients with long-term survival after immunotherapy in the absence of vitiligo, immunity is mostly directed against tumor-associated antigens that are not expressed by normal melanocytes. This review describes the immunological balance between vitiligo and melanoma, and to what extent the pathogenesis of vitiligo is beneficial for melanoma regression.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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.
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".