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Record W3119966384 · doi:10.1016/j.euo.2020.12.008

A Systematic Review of Focal Ablative Therapy for Clinically Localised Prostate Cancer in Comparison with Standard Management Options: Limitations of the Available Evidence and Recommendations for Clinical Practice and Further Research

2021· review· en· W3119966384 on OpenAlexafffund
Anthony S. Bates, Jennifer Ayers, Νικόλαος Κωστακόπουλος, Thomas Lumsden, Ivo G. Schoots, Peter-Paul Willemse, Yuhong Yuan, Roderick C.N. van den Bergh, Jeremy Grummet, Henk G. van der Poel, Olivier Rouvière, Lisa Moris, Marcus Cumberbatch, Michael Lardas, Matthew Liew, Thomas Van den Broeck, Giorgio Gandaglia, Nicola Fossati, Erik Briers, Maria De Santis, Stefano Fanti, Silke Gillessen, Daniela E. Oprea‐Lager, Guillaume Ploussard, Ann Henry, Derya Tilki, Theodorus van der Kwast, Thomas Wiegel, James N’Dow, Malcolm D. Mason, Philip Cornford, Nicolas Mottet, Thomas Lam

Bibliographic record

VenueEuropean Urology Oncology · 2021
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsMcMaster University Medical Centre
FundersTakeda CanadaRoche ProductsPfizer UKShionogiAstellas PharmaEuropean Association of UrologySir John Fisher FoundationIpsen FundCancer AustraliaCancer Research UKNovartisJanssen BiotechLes Laboratories Pierre FabreMedical Research CouncilBayerAstraZenecaBristol-Myers SquibbAmgen
KeywordsMedicineProstatectomyProstate cancerRetrospective cohort studyRandomized controlled trialRadiation therapySystematic reviewAdverse effectSurgeryMEDLINEInternal medicineCancer

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.008
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.031
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0120.011
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.399
GPT teacher head0.554
Teacher spread0.155 · 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 designSystematic review
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

Citations53
Published2021
Admission routes2
Has abstractno

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