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Record W4293659406 · doi:10.3390/cancers14174149

The Effect of Age on Prostate Cancer Survival

2022· article· en· W4293659406 on OpenAlexaff
Roderick Clark, Danny Vesprini, Steven A. Narod

Bibliographic record

VenueCancers · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsPublic Health OntarioWomen's College HospitalUniversity of Toronto
Fundersnot available
KeywordsProstate cancerMedicineProstateCancerMortality rateEpidemiologySurvival rateInternal medicineOncologyGynecology

Abstract

fetched live from OpenAlex

It is not clear to what extent the age of diagnosis and the attained age impact on cancer mortality rates in men with newly diagnosed prostate cancer. We estimated annual prostate cancer mortality rates and 20-year survival rates according to the age of diagnosis, race, grade and time since diagnosis using data from the Surveillance, Epidemiology and End-Results (SEER) program. We identified 116,796 prostate cancer patients diagnosed between 1992 and 1997 and followed them for 20 years. There were 21,896 deaths from prostate cancer. We calculated actuarial survival rates and annual prostate cancer mortality rates by age of diagnosis and by tumor grade. The risk of a man dying of prostate cancer was 17% for men diagnosed before age 70 and was 21% for those diagnosed after age 70. The mean annual prostate cancer mortality rate calculated over the 20-year period post-diagnosis was 1.5%. The annual rate increased from 0.9% for those diagnosed below age 60 to 2.1% for those diagnosed above age 70. For men with Gleason score ≥ 7 prostate cancer, the annual prostate cancer mortality rate peaked 2-3 years after diagnosis and then declined. For men diagnosed with Gleason score ≤ 6 prostate cancer, the annual prostate cancer mortality rate continued to rise 20 years after diagnosis and peaked after age 85. This suggests that high-grade prostate cancers are aggressive from the outset, but that low-grade prostate cancers may enter a state of dormancy and reactivate as the patient ages.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.304
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 designNot applicable
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

Citations43
Published2022
Admission routes1
Has abstractyes

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