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Annual incidence of colorectal cancer among patients with average risk in the United States between 2017 and 2023.

2025· article· en· W4406869349 on OpenAlexaff
Mallik Greene, Quang A. Le, Joshua Mou, Lakshya Sakthisivabalan, Danielle Lynch, A. Burak Ozbay, Joseph W. LeMaster, Igor Stukalin, J. P. Anderson, Paul J. Limburg

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of Calgary
FundersExact Sciences Corporation
KeywordsMedicineColorectal cancerIncidence (geometry)CancerInternal medicineOncologyDemography

Abstract

fetched live from OpenAlex

83 Background: Colorectal cancer (CRC) is the second most common cause of cancer-related mortality and third most common cancer in the United States. In 2021, 141,902 new cases of CRC were reported. With recent increases in early onset of CRC incidence, a shift in patterns amongst various demographics has been observed. Together with an estimated 60% increase in burden of CRC globally, it is important to continuously assess the nationwide incidence rates and monitor the changing patterns. In this study, we examined the annual incidence rates of CRC in average risk adults using a large national claims database and report patterns of incidence based on various demographic characteristics. Methods: CRC diagnoses were retrospectively identified from a large national claims database, which covers over 165 million lives and is representative of the U.S. population, for each calendar year during the study period from 2017 to 2023. Individuals aged 45 to 75 years, with an average risk of CRC before each index calendar year were included in the study. Individuals were required to be continuously enrolled in the same health plan for at least 1 year prior to and 1 year following the index year. Annual CRC incidence rates per 100,000 people were calculated for each year from 2017 to 2023 within various subgroups based on baseline demographics. Results: Overall, annual CRC incidences remained consistent during the study period with 186.86 (95% CI, 184.87-188.86) cases in 2017 and 183.40 (95% CI, 181.32-185.5) cases in 2023 per 100,000. Among younger adults aged 45-49 years, the annual CRC incidence increased by 66% from 2017 (92.04 cases/100,000) to 2023 (153.12 cases/100,000). Conversely, annual CRC incidence decreased by 25% between 2017 (328.52 cases/100,000) and 2023 (244.93/100,000) in older adults aged 65-75 years. Over the study period, CRC incidence in men was higher than women with an overall rate ratio (RR) of 1.206 (95% CI, 1.122-1.297). African American adults had higher CRC incidence than White Americans with an overall rate ratio (RR) of 1.216 (95% CI, 1.152-1.283). Conclusions: Based on this large study of national claims data, CRC incidence rates remained relatively stable in the total population among average-risk patients from 2017 to 2023. The continuing increase in recommended CRC screening utilization showed a decreased incidence rate in the older population, while observing increased incidences among younger patients.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.409
Teacher spread0.370 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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