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Record W2111311540 · doi:10.1093/jnci/djm155

Fruits, Vegetables, and Colon Cancer Risk in a Pooled Analysis of 14 Cohort Studies

2007· article· en· W2111311540 on OpenAlexaff
Anita Koushik, David J. Hunter, D. Spiegelman, W. L. Beeson, Piet A. van den Brandt, J. E. Buring, E. E. Calle, Eunyoung Cho, Gary E. Fraser, Jo L. Freudenheim, Christiane Fuchs, Edward L. Giovannucci, R. Alexandra Goldbohm, Lisa Harnack, David R. Jacobs, Ikuko Kato, Vittorio Krogh, Susanna C. Larsson, Michael F. Leitzmann, James R. Marshall, Marjorie L. McCullough, Anthony B. Miller, P Pietinen, Tomáš Rohan, Arthur Schatzkin, Sabina Sieri, Mikko Virtanen, A. Wolk, Anne Zeleniuch‐Jacquotte, S. M. Zhang, Stephanie A. Smith‐Warner

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2007
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsUniversity of TorontoUniversité de Montréal
FundersNational Cancer Institute
KeywordsColorectal cancerMedicineCohortPooled analysisCohort studyEnvironmental healthCancerOncologyInternal medicineMeta-analysis

Abstract

fetched live from OpenAlex

BACKGROUND: Fruit and vegetable intakes have been associated with a reduced risk of colon cancer; however, in more recent studies associations have been less consistent. Statistical power to examine associations by colon site has been limited in previous studies. METHODS: Fruit and vegetable intakes in relation to colon cancer risk were examined in the Pooling Project of Prospective Studies of Diet and Cancer. Relative risks (RRs) and 95% confidence intervals (CIs) were estimated separately in 14 studies using Cox proportional hazards model and then pooled using a random-effects model. Intakes of total fruits and vegetables, total fruits, and total vegetables were categorized according to quintiles and absolute cutpoints. Analyses were conducted for colon cancer overall and for proximal and distal colon cancer separately. All statistical tests were two-sided. RESULTS: Among 756,217 men and women followed for up to 6 to 20 years, depending on the study, 5838 were diagnosed with colon cancer. The pooled multivariable RRs (95% CIs) of colon cancer for the highest versus lowest quintiles of intake were 0.91 (0.82 to 1.01, P(trend) = .19) for total fruits and vegetables, 0.93 (0.85 to 1.02, P(trend) = .28) for total fruits, and 0.94 (0.86 to 1.02, P(trend) = .17) for total vegetables. Similar results were observed when intakes were categorized by identical absolute cut points across studies (pooled multivariable RR = 0.90, 95% CI = 0.77 to 1.05 for 800 or more versus <200 g/day of total fruits and vegetables, P(trend) = .06). The age-standardized incidence rates of colon cancer for these two intake categories were 54 and 61 per 100,000 person-years, respectively. When analyzed by colon site, the pooled multivariable RRs (95% CIs) comparing total fruit and vegetable intakes of 800 or more versus less than 200 g/day were 0.74 (0.57 to 0.95, P(trend) = .02) for distal colon cancers and 1.02 (0.82 to 1.27, P(trend) = .57) for proximal colon cancers. Similar site-specific associations were observed for total fruits and total vegetables. CONCLUSION: Fruit and vegetable intakes were not strongly associated with colon cancer risk overall but may be associated with a lower risk of distal colon cancer.

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)0.035
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.048
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0070.028
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.001
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 head0.046
GPT teacher head0.371
Teacher spread0.326 · 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 designMeta-analysis
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

Citations272
Published2007
Admission routes1
Has abstractyes

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