MétaCan
Menu
Back to cohort
Record W2624891301 · doi:10.21037/tlcr.2017.06.03

Radio-immunotherapy and chemo-immunotherapy as a novel treatment paradigm in malignant pleural mesothelioma

2017· review· en· W2624891301 on OpenAlexaff
Licun Wu, Marc de Perrot

Bibliographic record

VenueTranslational Lung Cancer Research · 2017
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsToronto General HospitalUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineImmunotherapyMesotheliomaRadiation therapyChemotherapyOncologyCancer researchImmune systemImmunologyInternal medicinePathology

Abstract

fetched live from OpenAlex

Malignant pleural mesothelioma (MPM) is an aggressive neoplasm with poor outcome. Novel radical radiation techniques using intensity modulated radiation therapy (IMRT) have become an important component of therapy in mesothelioma. Immunotherapy also provides new therapeutic options. However, how best to integrate immunotherapy with standard therapy such as radiation, chemotherapy and surgery remains unknown. A change of paradigm from adjuvant normofractionation to induction accelerated hypofractionated hemithoracic radiation could provide a platform to combine immunotherapy due to the potential benefit of short course high dose radiation on the immune system. Immunotherapy can also be combined with chemotherapy. Although chemotherapy is generally considered immunosuppressive, some chemotherapeutic agents do induce cell death that can be immunogenic and stimulate a specific immune response against the tumor. Immunotherapy could also be used in between cycles of chemotherapy to limit tumor cell repopulation and optimize the results of both treatments. The integration of immunotherapy into a multimodality approach is opening new avenue of treatment for mesothelioma.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.193
GPT teacher head0.498
Teacher spread0.305 · 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

Citations25
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueTranslational Lung Cancer ResearchSame topicOccupational and environmental lung diseasesFrench-language works237,207