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Record W3005956436 · doi:10.3747/co.27.5407

Immunotherapy in Soft-Tissue Sarcoma

2020· review· en· W3005956436 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCurrent Oncology · 2020
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreMount Sinai Hospital
Fundersnot available
KeywordsMedicineMalignancyImmunotherapyPathognomonicSoft tissue sarcomaSarcomaMelanomaCancerCancer immunotherapyRadiation therapyDiseaseOncologyPathologyCancer researchInternal medicine

Abstract

fetched live from OpenAlex

Soft-tissue sarcoma (sts) is a rare mesenchymal malignancy that accounts for less than 1% of all adult tumours. Despite the successful advancement of localized therapies such as surgery and radiotherapy, these tumours can, for many, recur-often with metastatic disease. In the advanced setting, the role of systemic therapies is modest and is associated with poor survival. With the discovery of immunotherapies in other tumour types such as melanoma and lung cancer, interest has been renewed in exploring immunotherapy in sts. The biology of some stss makes them ripe for immunotherapy intervention; for example, some stss might have chromosomal translocations resulting in pathognomonic fusion products that have been shown to express cancer/testis antigens. Here, we present a targeted review of the published data and ongoing clinical trials for immunotherapies in patients with sarcoma, which comprise immune checkpoint inhibitors, adoptive cell therapies, and cancer vaccines.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.196
GPT teacher head0.485
Teacher spread0.290 · 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