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Record W3005956436 · doi:10.3747/co.27.5407

Immunotherapy in Soft-Tissue Sarcoma

2020· review· en· W3005956436 on OpenAlexaffvenue
Olubukola Ayodele, A.R. Abdul Razak

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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

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 designNot applicable
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

Citations117
Published2020
Admission routes2
Has abstractyes

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