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Record W4205498652 · doi:10.1200/jco.21.01719

Practical Management of Oligometastatic Non–Small-Cell Lung Cancer

2022· review· en· W4205498652 on OpenAlexaff
Katie Jasper, Brendon M. Stiles, Fiona McDonald, David A. Palma

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsLondon Health Sciences CentreUniversity of British ColumbiaWestern University
Fundersnot available
KeywordsMedicineSABR volatility modelRadiosurgeryLung cancerAblative caseRadiation therapyOncologyDiseaseRandomized controlled trialInternal medicineMedical physicsIntensive care medicine

Abstract

fetched live from OpenAlex

Local ablative therapies, including surgery or stereotactic radiotherapy (SABR), are becoming an integral component in the treatment of oligometastatic disease in non-small-cell lung cancer. In this review, we summarize recent randomized evidence supporting progression-free survival and overall survival benefits of local ablation in these patients, as well as upcoming phase III data which should help us better understand the ideal treatment conditions and provide more insight into the oligometastatic state. Since practical management of oligometastatic disease in non-small-cell lung cancer can be challenging, we discuss a modern framework to identify patient, tumor, and treatment characteristics that can best guide management.

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 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.925
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.287
GPT teacher head0.583
Teacher spread0.297 · 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 teacher head, not a consensus.

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

Citations99
Published2022
Admission routes1
Has abstractyes

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