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Record W2952917587 · doi:10.1111/bju.14815

<scp>NICE</scp> guidelines on prostate cancer 2019

2019· editorial· en· W2952917587 on OpenAlexaboutno aff
Prokar Dasgupta, John A. Davis, Simon Hughes

Bibliographic record

VenueBritish Journal of Urology · 2019
Typeeditorial
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsNiceProstate cancerGuidelineMedicineExcellenceProstateCancerMedical physicsFamily medicineGynecologyInternal medicinePathologyComputer science

Abstract

fetched live from OpenAlex

NICE guidelines on prostate cancer 2019 T he much-anticipated National Institute for Health and Care Excellence (NICE) Guidelines are finally published [1] after a period of consultation when they were in the draft phase.These are updated from the previous 2008 and 2014 versions and reflect the changes in our knowledge and practice over the last 10 years.While there are many similarities, the astute reader will find distinct differences from the AUA Guidelines, which feature in a summary booklet released at the #AUA19 meeting in Chicago this spring.

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)0.005
metaresearch head score (Gemma)0.039
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: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.143
Threshold uncertainty score0.480

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0060.004
Open science0.0050.004
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.1430.117

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 head0.016
GPT teacher head0.314
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 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
GenreEditorial

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

Citations44
Published2019
Admission routes1
Has abstractyes

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