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Impact of U.S. Preventative Services Task Force grade D recommendation against prostate-specific antigen screening on prostate cancer mortality.

2022· article· en· W4213222613 on OpenAlexaff
Laura Burgess, Christopher M. Aldrighetti, Anushka Ghosh, Andrzej Niemierko, Fumiko Chino, Melissa Huynh, Jason A. Efstathiou, Sophia C. Kamran

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsWestern UniversityUniversity of Ottawa
Fundersnot available
KeywordsMedicineProstate cancerDemographyProstate cancer screeningProstate-specific antigenCancerIncidence (geometry)GynecologyInternal medicineOncologyGerontology

Abstract

fetched live from OpenAlex

51 Background: The U. S. Preventative Services Task Force (USPSTF) recommendation regarding prostate-specific antigen (PSA) transitioned to a grade D recommendation against PSA screening for adult males in 2012. The impact of this recommendation against PSA screening on prostate cancer-specific mortality (PCSM) in contemporary cohorts is unknown. Our study evaluated PCSM between 1999-2019, comparing mortality rates before and after this change to screening guidelines. Methods: Age-adjusted PCSM rates per 100,000 men were obtained from the National Center for Health Statistics from 1999 – 2019. Trends in PCSM rates from 1999 – 2012 and 2014 – 2019 were estimated using linear regression with year and binary indicator of pre-2013/post-2013 status as interaction terms. Age-adjusted rates of PCSM were calculated for men ≥50 years and by race, ethnicity, urbanization and census region. Similarly, age-adjusted rates of overall cancer mortality (exclusive of PCSM) were calculated. Behavioral Risk Factor Surveillance System was used to establish trends in PSA screening from 2001 – 2018. North American Association of Central Cancer Registries was used to determine age-adjusted incidence of localized and metastatic PC at the time of diagnosis from 1999 – 2017. Results: The age-adjusted PCSM rate in the U.S. decreased linearly at a rate of (-)0.28 per 100,000/year from 1999 – 2012 and subsequently stalled at a rate of no change from 2014 – 2019 (p < 0.001). This effect was particularly striking for men aged 60 – 69, men > 80 years, and Black men. Men aged 60 – 64 had a decreasing rate of (-)0.009 per 100,000/year prior to 2013, followed by a rise of (+)0.001 per 100,000/year (p < 0.001). Among Black men, PCSM rate was decreasing linearly at (-)0.700/100,000/year from 1999-2012 and flattened at a rate of (-)0.091/100,000/year from 2014-2019 (p < 0.001). These changes were seen across races, urbanization and census regions (p < 0.001) and were accompanied by decreases in PSA screening (p = 0.02) together with increases in diagnosis of metastatic disease. These trends were inconsistent with mortality trends observed across all malignancies. Conclusions: Using comprehensive data on PCSM through 2019, this study illustrates decreasing PCSM over time which flattened or increased following the 2012 change in USPSTF guideline, along with a decrease in PSA screening. The change in PCSM was seen in all ages, races, ethnicities, urbanization and census regions, but particularly in men from 60 – 69 and > 80 years old, and Black men. These changes were accompanied by increased diagnosis of metastatic PC and are discordant from trends across other malignancies. These findings suggest that the change in PSA screening guideline may have contributed to the stagnancy of PCSM rates in recent years. The updated 2018 USPSTF guideline supporting shared-decision making may reverse these trends over time.

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)0.019
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.381
Threshold uncertainty score0.757

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0030.003
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 head0.147
GPT teacher head0.498
Teacher spread0.351 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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