MétaCan
Menu
Back to cohort
Record W2891793107 · doi:10.1136/bmj.k3581

Prostate cancer screening with prostate-specific antigen (PSA) test: a clinical practice guideline

2018· review· en· W2891793107 on OpenAlexaff
Kari A.O. Tikkinen, Philipp Dahm, Lyubov Lytvyn, Anja Fog Heen, Robin W.M. Vernooij, Reed Siemieniuk, Russell Wheeler, Bill Vaughan, Awah Cletus Fobuzi, Marco H. Blanker, Noelle Perron Junod, Johanna Sommer, Jérôme Stirnemann, Manabu Yoshimura, Reto Auer, Helen Macdonald, Gordon Guyatt, Per Olav Vandvik, Thomas Agoritsas

Bibliographic record

VenueBMJ · 2018
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineProstate cancerProstate-specific antigenGuidelineProstate cancer screeningRandomized controlled trialCancerProstateOncologyGynecologyClinical trialInternal medicinePathology

Abstract

fetched live from OpenAlex

### What you need to know What is the role of prostate-specific antigen (PSA) screening in prostate cancer? An expert panel produced these recommendations based on a linked systematic review.1 The review was triggered by a large scale, cluster randomised trial on PSA screening in men without a previous diagnosis of prostate cancer published in 2018 (box 1).2 It found no difference between one-time PSA screening and standard practice in prostate cancer mortality but found an increase in the detection of low risk prostate cancer after a median follow-up of 10 years. Box 1 ### Results of the CAP Randomized Clinical Trial2 This cluster-randomised trial of 419 582 British men was published in March 2018. After a median follow-up of 10 years, there was no significant difference in prostate cancer-specific mortality in men receiving care by general practices randomised to a single PSA screening intervention compared with men receiving care … RETURN TO TEXT

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)0.009
metaresearch head score (Gemma)0.028
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 score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0040.001
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0050.002

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 head0.150
GPT teacher head0.480
Teacher spread0.330 · 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

Citations185
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueBMJSame topicProstate Cancer Diagnosis and TreatmentFrench-language works237,207