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Record W4392401568 · doi:10.1093/jnci/djae051

Dietary interventions in cancer: a systematic review of all randomized controlled trials

2024· review· en· W4392401568 on OpenAlexaff
Nosakhare Paul Ilerhunmwuwa, Abul Hasan Shadali Abdul Khader, Calvin Smith, Edward R. Scheffer Cliff, Christopher M. Booth, Evevanne Hottel, Muhammad Aziz, Wade Lee‐Smith, Aaron M. Goodman, Rajshekhar Chakraborty, Ghulam Rehman Mohyuddin

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2024
Typereview
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineCancerClinical endpointInterquartile rangeRandomized controlled trialInternal medicinePsychological interventionClinical trialSystematic reviewProstate cancerOncologyMEDLINE

Abstract

BACKGROUND: Prior systematic reviews addressing the impact of diet on cancer outcomes have focused on specific dietary interventions. In this systematic review, we assessed all randomized controlled trials (RCTs) investigating dietary interventions for cancer patients, examining the range of interventions, endpoints, patient populations, and results. METHODS: This systematic review identified all RCTs conducted before January 2023 testing dietary interventions in patients with cancer. Assessed outcomes included quality of life, functional outcomes, clinical cancer measurements (eg, progression-free survival, response rates), overall survival, and translational endpoints (eg, inflammatory markers). RESULTS: In total, 252 RCTs were identified involving 31 067 patients. The median sample size was 71 (interquartile range 41 to 118), and 80 (32%) studies had a sample size greater than 100. Most trials (n = 184, 73%) were conducted in the adjuvant setting. Weight or body composition and translational endpoints were the most common primary endpoints (n = 64, 25%; n = 52, 21%, respectively). Direct cancer measurements and overall survival were primary endpoints in 20 (8%) and 7 (3%) studies, respectively. Eight trials with a primary endpoint of cancer measurement (40%) met their endpoint. Large trials in colon (n = 1429), breast (n = 3088), and prostate cancer (n = 478) each showed no effect of dietary interventions on endpoints measuring cancer. CONCLUSION: Most RCTs of dietary interventions in cancer are small and measure nonclinical endpoints. Although only a small number of large RCTs have been conducted to date, these trials have not shown an improvement in cancer outcomes. Currently, there is limited evidence to support dietary interventions as a therapeutic tool in cancer care.

Stored with the screening record, where it is evidence for the labels above.

How this classification was reachedexpand

The three-model screen

all 5,600 screened works →

1 of 3 models called this metaresearch. This work is contested: it sits on the field's empirical boundary, and whether it counts depends on which model you asked. It is one of the 51 works in the disagreement dossier.

stratum: aff_core · design weight: 5595.24 (the sample is stratified; any rate computed without the weight is wrong)
Claude Opus 4.8T1
genre: empirical
about Canada: no
confidence: medium

Systematic review characterizing all dietary intervention RCTs in cancer by sample size, endpoint choice, and design; the reported findings are about the state of the trial literature, though a clinical conclusion is also drawn.

GPT-5.6 (high)OUT
genre: empirical
about Canada: no
confidence: high

The systematic review answers a clinical question about dietary interventions in cancer rather than studying synthesis methodology.

Grok 4.5OUT
genre: empirical
about Canada: no
confidence: high

Systematic review of dietary RCTs in cancer answers a clinical effectiveness question; uses synthesis method rather than studying the method.

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)0.033
metaresearch head score (Gemma)0.130
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.130
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0170.015
Bibliometrics0.0150.016
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.321
GPT teacher head0.520
Teacher spread0.199 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations27
Published2024
Admission routes1
Has abstractyes

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