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Record W4393094075 · doi:10.1158/1538-7445.am2024-4847

Abstract 4847: Diverging global incidence trends of early-onset and later-onset cancers

2024· article· en· W4393094075 on OpenAlexaboutno aff
Miyu Terashima, Hwa‐Young Lee, Yuta Tsukumo, Satoko Ugai, Minkyo Song, Naoko Sasamoto, Ichiro Kawachi, Tomotaka Ugai

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicMultiple and Secondary Primary Cancers
Canadian institutionsnot available
Fundersnot available
KeywordsIncidence (geometry)MedicineAge of onsetCancerPediatricsDemographyOncologyInternal medicineDiseasePhysicsSociology

Abstract

fetched live from OpenAlex

Abstract Background: The global increase of the incidence of early-onset cancers (defined as cancers diagnosed at 20-49 years) is a serious public health problem. However, it is understudied whether the incidence of early-onset cancers has increased in parallel with that of later-onset cancers (defined as cancers diagnosed at 50 years or above) or only the incidence of early-onset cancer has increased worldwide. We therefore evaluated the recent global trends of the incidence of early-onset cancers and later-onset cancers. Method: We retrieved sex-stratified age-standardized incidence rates of early-onset cancer and later-onset cancer diagnosed between 2000 and 2012 in 39 countries where data was available from the GLOBOCAN database. Using joinpoint regression models, we assessed average annual percentage change (AAPC) by cancer types and countries, with statistical significance corresponding to a 95% CI that does not include zero. Results: We observed statistically significant positive AAPCs for early-onset cancers (AAPC [95% CI], 1.9% [1.8%, 2.1%] for females, 0.7% [0.6%, 0.9%] for males in all available countries combined) in many early-onset cancer types, including colorectal, esophagus, gallbladder, kidney, liver, multiple myeloma, pancreas, prostate, stomach, testis, thyroid and uterine cancer in many parts of the world. There were variations in many later-onset cancer types depending on cancer types and countries. Notable cancer types that have significantly increased in early-onset cancers but have decreased or showed no change in later-onset cancers include colorectal (5 countries in females and 6 countries in males), uterine (4 countries), and thyroid cancers (5 countries in females and 3 countries in males). In particular, early-onset colorectal cancer has increased but later-onset colorectal cancer has decreased among both sexes in Canada (AAPC [95%CI], early-onset: 1.6% [0.9%, 2.7%], later-onset: -0.8% [-1.0%, -0.5%] for females; early-onset: 1.8% [1.0%, 2.6%], later-onset: -1.2% [-1.5%, -0.9%] for males), USA (Early-onset: 2.0% [1.5%, 2.5%], later-onset: -2.7% [-2.9%, -2.6%] for females; early-onset: 1.6% [1.1%, 2.2%], later-onset: -3.2% [-3.7%, -2.6%] for males), Australia (Early-onset: 1.8% [0.7%, 2.9%], later-onset: -1.0% [-1.5%, -0.6%] for females; early-onset: 1.4% [0.7%, 2.4%], later-onset: -1.5% [-1.9%, -0.9%] for males). Conclusion: Our study highlights differences in cancer incidence trends between certain early-onset and later-onset cancer types in many parts of the world. Notably, early-onset colorectal, uterine, and thyroid cancers have significantly increased but corresponding later-onset cancers have decreased or showed no change in many countries. Different patterns in cancer incidence trends of early-onset and later-onset cancers in different regions should be further investigated to better understand and prevent the increase in the incidence of early-onset cancers. Citation Format: Miyu Terashima, Hwa-Young Lee, Yuta Tsukumo, Satoko Ugai, Minkyo Song, Naoko Sasamoto, Ichiro Kawachi, Tomotaka Ugai. Diverging global incidence trends of early-onset and later-onset cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4847.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.427
Teacher spread0.354 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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