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Record W4229030236 · doi:10.3390/curroncol29050256

Early-Age-Onset Colorectal Cancer in Canada: Evidence, Issues and Calls to Action

2022· article· en· W4229030236 on OpenAlexaffvenueabout
Mary A. De Vera, Sharlene Gill, Shady Ashamalla, Dan Schiller, Darren R. Brenner, Clarence Wong, Petra Wildgoose, Mary Jane Esplen, Christopher H. Lieu, Roslyn Fitzpatrick, Dylan E. O’Sullivan, Filomena Servidio-Italiano

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsThornhill Medical (Canada)University of CalgaryAlberta Health ServicesUniversity of AlbertaUniversity of TorontoSunnybrook Health Science CentreHealth Sciences CentreUniversity of British Columbia
Fundersnot available
KeywordsMedicineColorectal cancerIncidence (geometry)DiseaseCancerEtiologyFamily medicineGerontologyInternal medicine

Abstract

fetched live from OpenAlex

The inaugural Early-Age-Onset Colorectal Cancer Symposium was convened in June 2021 to discuss the implications of rapidly rising rates of early-age-onset colorectal cancer (EAO-CRC) in Canadians under the age of 50 and the impactful outcomes associated with this disease. While the incidence of CRC is declining in people over the age of 50 in Canada and other developed countries worldwide, it is significantly rising in younger people. Canadians born after 1980 are 2 to 2.5 times more likely to be diagnosed with CRC before the age of 50 than previous generations at the same age. While the etiology of EAO-CRC is largely unknown, its characteristics differ in many key ways from CRC diagnosed in older people and warrant a specific approach to risk factor identification, early detection and treatment. Participants of the symposium offered directions for research and clinical practice, and developed actionable recommendations to address the unique needs of these individuals diagnosed with EAO-CRC. Calls for action emerging from the symposium included: increased awareness of EAO-CRC among public and primary care practitioners; promotion of early detection programs in younger populations; and the continuation of research to identify unique risk factor profiles, tumour characteristics and treatment models that can inform tailored approaches to the management of EAO-CRC.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.918

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 head0.120
GPT teacher head0.418
Teacher spread0.298 · 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 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

Citations13
Published2022
Admission routes3
Has abstractyes

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