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RE: ASSOCIATION OF HEMOSPERMIA WITH PROSTATE CANCER

2005· article· en· W2626854315 on OpenAlexaffabout
Misop Han, R.E. Brannigan, Jo Ann V. Antenor, K.A. Roehl, William J. Catàlona

Bibliographic record

VenueThe Journal of Urology · 2005
Typearticle
Languageen
FieldMedicine
TopicUrologic and reproductive health conditions
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineBiostatisticsEpidemiologyProstate cancerGynecologyClinical epidemiologyCancerFamily medicineLibrary sciencePathologyInternal medicine

Abstract

fetched live from OpenAlex

No AccessJournal of UrologyLetters to the Editor/Errata1 Aug 2005RE: ASSOCIATION OF HEMOSPERMIA WITH PROSTATE CANCER M. Han, R.E. Brannigan, J.A.V. Antenor, K.A. Roehl, and W.J. Catalona M. HanM. Han More articles by this author , R.E. BranniganR.E. Brannigan More articles by this author , J.A.V. AntenorJ.A.V. Antenor More articles by this author , K.A. RoehlK.A. Roehl More articles by this author , and W.J. CatalonaW.J. Catalona More articles by this author View All Author Informationhttps://doi.org/10.1097/01.ju.0000164746.08123.70AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail "RE: ASSOCIATION OF HEMOSPERMIA WITH PROSTATE CANCER." The Journal of Urology, 174(2), p. 789 Division of Cancer Epidemiology; Departments of Oncology, and Epidemiology and Biostatistics; McGill University; 546 Pine Ave. West; Montreal, Quebec; Canada H2W 1S6© 2005 by American Urological Association, Inc.FiguresReferencesRelatedDetails Volume 174Issue 2August 2005Page: 789 Advertisement Copyright & Permissions© 2005 by American Urological Association, Inc.MetricsAuthor Information M. Han More articles by this author R.E. Brannigan More articles by this author J.A.V. Antenor More articles by this author K.A. Roehl More articles by this author W.J. Catalona More articles by this author Expand All Advertisement PDF downloadLoading ...

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.177

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.299
Teacher spread0.286 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2005
Admission routes2
Has abstractyes

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