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Record W4411448320 · doi:10.54434/candj.204

The Role of Naturopathic Medicine During Active Surveillance in Prostate Cancer

2025· article· en· W4411448320 on OpenAlexaffvenue
Megan Sandri, Daniel P. Lander

Bibliographic record

VenueCAND Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsCanadian College of Naturopathic Medicine
Fundersnot available
KeywordsMedicineProstate cancerDiseaseCancerRandomized controlled trialInternal medicineOncologyObservational studyPhysical therapyIntensive care medicine

Abstract

fetched live from OpenAlex

Background: Active surveillance (AS) is increasingly used in low-risk prostate cancer, presenting an opportunity for integrative interventions to delay progression and improve overall health. This review explores the role of diet, exercise, and nutritional supplements in AS. Methods: A narrative review of clinical and observational studies on dietary patterns, physical activity (PA), and nutritional supplements in AS and pre-surgical prostate cancer research. Results: Plant-based and Mediterranean diets may reduce Gleason-grade progression, though randomized controlled trials (RCTs) show mixed results. Exercise, particularly high-intensity interval training (HIIT), may influence prostate-specific antigen (PSA) kinetics. Nutritional supplements such as vitamin D, green tea polyphenols, lycopene, mushroom mycelium extract, glucoraphanin, and fish oil show potential but inconsistent effects. Cardiovascular disease mortality surpasses prostate cancer mortality in men with localized disease, underscoring the need for cardiometabolic support alongside AS. Multimodal approaches integrating diet, exercise, and supplementation may offer the greatest benefit. Conclusion: While no single intervention is proven to prevent prostate cancer progression, a comprehensive, personalized approach, including diet, exercise, and additional integrative therapies, may optimize outcomes in motivated patients. Future research should focus on evaluating multimodal integrative strategies in AS.

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 score0.324
Threshold uncertainty score0.231

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 head0.004
GPT teacher head0.267
Teacher spread0.263 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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