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Record W2019449237 · doi:10.1373/clinchem.2009.140046

Prostate Cancer Screening with Prostate-Specific Antigen Testing: More Answers or More Confusion?

2010· article· en· W2019449237 on OpenAlexaff
Eleftherios P. Diamandis

Bibliographic record

VenueClinical Chemistry · 2010
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsConfusionProstate-specific antigenProstateProstate cancerMedicineAntigenCancerProstate cancer screeningOncologyInternal medicineImmunologyPsychology

Abstract

fetched live from OpenAlex

Prostate cancer is a leading cause of morbidity and mortality among middle-aged and older men. Of the solid tumors prostate cancer is rather unique in that it presents in 2 distinct forms, a latent form, which grows slowly and poses no threat to the patient’s life, and an aggressive form, which metastasizes quickly and kills the patient. The discovery of prostate-specific antigen (PSA)2 and the demonstration of its utility for early diagnosis and monitoring of prostatic carcinoma have raised hopes that this simple serological test could be invaluable in screening asymptomatic individuals for early prostate cancer diagnosis. The premise is that such early diagnosis may then lead to early therapeutic interventions, which should improve the overall survival of prostate cancer patients. However, PSA screening of asymptomatic individuals has remained controversial during the last 15 years owing to the lack of evidence for improved patient survival. Recently, the results of 2 major randomized clinical trials on the effectiveness of PSA as a screening tool, from both the US and Europe, have been published. These results are not clear cut. For this reason, the controversy surrounding prostate cancer screening will likely continue for years. Below, we examine this issue with 4 authorities in the field.

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)0.045
metaresearch head score (Gemma)0.098
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.045
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0450.098
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0040.004
Science and technology studies0.0040.029
Scholarly communication0.0110.025
Open science0.0040.006
Research integrity0.0210.028
Insufficient payload (model declined to judge)0.0070.004

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 head0.097
GPT teacher head0.417
Teacher spread0.320 · 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
GenreCommentary

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

Citations22
Published2010
Admission routes1
Has abstractyes

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