MétaCan
Menu
Back to cohort

Cancer Prevention

2025· other· en· W4415725884 on OpenAlexaff
Carolyn Gotay

Bibliographic record

VenueThe Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and Society · 2025
Typeother
Languageen
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCancerCancer preventionCancer incidenceEpidemiologyRisk assessmentDisease

Abstract

fetched live from OpenAlex

Abstract Cancer is one of the leading threats to health worldwide, and its impact is predicted to increase dramatically in coming years; by 2040, 28.4 million new cases are forecast, a 47 percent increase in only two decades. Yet, much evidence shows that only about 10 percent of cancer is due to inherited genetic causes, and many cancers – as much as 50 percent – could be prevented by reducing exposure to known risk factors. This article reviews what is known about cancer's preventability, risk factors, approaches to cancer prevention, and future directions for cancer prevention.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.251
Threshold uncertainty score0.840

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2510.081

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 head0.013
GPT teacher head0.300
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueThe Wiley Blackwell Encyclopedia of Health, Illness, Behavior, and SocietyFrench-language works237,207