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Record W2312670817 · doi:10.1586/14737140.2016.1168698

Stereotactic body radiotherapy for pancreatic cancer: recent progress and future directions

2016· review· en· W2312670817 on OpenAlexaff
Sten Myrehaug, Arjun Sahgal, Suzanne Russo, Simon S. Lo, Lauren M. Rosati, Nina A. Mayr, Michael Lock, William Small, Jennifer A. Dorth, Rodney J. Ellis, Bin S. Teh, Joseph M. Herman

Bibliographic record

VenueExpert Review of Anticancer Therapy · 2016
Typereview
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsWestern UniversitySunnybrook Health Science CentreHealth Sciences Centre
Fundersnot available
KeywordsMedicineRadiation therapyPancreatic cancerPancreasRegimenRadiosurgeryChemotherapyCancerOncologyRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Despite advances in surgical, medical, and radiation therapy for pancreatic cancer, the prognosis remains poor. At this time, the only chance for long-term survival is surgical resection. More challenging is the optimal management of unresectable locally advanced pancreatic cancer, which has historically been treated with concurrent chemoradiation or chemotherapy alone. However, the survival and local control benefit of conventional radiotherapy in addition to chemotherapy was unclear. More recently, stereotactic body radiotherapy (SBRT) is emerging as a viable approach to maximizing local tumor control with a tolerable side effect profile. SBRT achieves sharp dose fall-off facilitating safe delivery of highly focused radiation to the tumor over 1-5 days. Although the optimal regimen of pancreas SBRT has not yet been established, its short treatment course limits the delay of additional. Future directions involve prospective study of pancreas SBRT and exploration of biomarkers and imaging technology in order to adopt a personalized management paradigm.

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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.060
GPT teacher head0.468
Teacher spread0.408 · 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

Citations36
Published2016
Admission routes1
Has abstractyes

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