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Record W156376314

Workshop report: physical activity and cancer prevention.

2000· article· en· W156376314 on OpenAlexaffabout
Loraine D. Marrett, B Theis, Fredrick D. Ashbury

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsCancer Care Ontario
Fundersnot available
KeywordsMedicineCancer preventionPhysical activityPsychological interventionCancerProstate cancerPopulationPublic healthPopulation healthEnvironmental healthGerontologyFamily medicinePhysical therapyPathologyNursingInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

A workshop to evaluate the evidence for the role of physical activity in cancer prevention and to identify priorities for action, particularly in relation to the primary prevention of cancer, was held by Cancer Care Ontario in March 2000. A review of the scientific evidence was commissioned and an expert panel convened to consider the review report and to make recommendations for public health, research and intervention. The panel concluded that evidence was convincing for the role of physical activity in preventing colon cancer; probable for breast cancer; possible for prostate cancer and insufficient for other sites. It is recommended that physical activity messages promoting at least 30 45 minutes of moderate to vigorous activity on most days of the week be included in primary prevention interventions for cancer. The panel recommended that future research on physical activity incorporate comprehensive assessments, including measures of the multiple dimensions and types of physical activity; biological mechanisms; and behavioural and population factors. Cancer Care Ontario will incorporate physical activity messages in its primary prevention programming around nutrition and health body weight.

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)0.007
metaresearch head score (Gemma)0.006
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: none
Teacher disagreement score0.042
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.003
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0420.023

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 head0.061
GPT teacher head0.345
Teacher spread0.284 · 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

Citations32
Published2000
Admission routes2
Has abstractyes

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