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Record W2607972053 · doi:10.1080/17446651.2017.1324294

Hypofractionation for prostate cancer: an update

2017· article· en· W2607972053 on OpenAlexaff
David Tiberi, Peter Vavassis, David H. Nguyen, Michael Yassa

Bibliographic record

VenueExpert Review of Endocrinology & Metabolism · 2017
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicineProstate cancerImage-guided radiation therapyRadiation therapyRegimenClinical trialProstateDose fractionationRandomized controlled trialOncologyMedical physicsCancerInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Recent advances in image guided radiation therapy (IGRT) has prompted much interest in the use of high-dose-per-fraction regimens for prostate cancer. Furthermore, from a radiobiological standpoint, there is increasing evidence that prostate tumors have a relatively low ɑ/β ratio therefore, the use of hypofractionation may potentially offer acceptable tumor control while minimizing late toxicity to critical structures. Areas covered: This expert review explores the current evidence regarding the safety and efficacy of hypofractionated radiotherapy for prostate cancer. A particular emphasis was placed on large, randomized phase III trials as these are most likely to influence clinical practice. The authors discuss the use of both moderate and extreme hypofractionation. Expert commentary: The recent publication of 5-year outcomes from large prospective trials of moderate hypofractionation enhances our confidence that these techniques are both safe and effective. We recommend the fractionation scheme of 60 Gy in 20 fractions as this regimen was not associated with any notable increase in late toxicity. With respect to extreme hypofractionation, mature phase III trials are needed to demonstrate the safety and efficacy of these techniques. For now, the use of radiosurgery should be limited to participation in prospective clinical trials.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.401
Teacher spread0.365 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations1
Published2017
Admission routes1
Has abstractyes

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