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Record W2103113862 · doi:10.1002/pbc.21401

Complementary and alternative (CAM) dietary therapies for cancer

2007· review· en· W2103113862 on OpenAlexaff
Sheila Weitzman

Bibliographic record

VenuePediatric Blood & Cancer · 2007
Typereview
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of TorontoSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMedicineCancerBreast cancerProstate cancerObservational studyCancer treatmentAlternative medicineIntensive care medicineInternal medicinePathology

Abstract

fetched live from OpenAlex

Complementary and alternative (CAM) therapies include a wide spectrum of dietary practices, some of which are claimed to cure cancer. Observational studies have shown consistently that predominantly plant-based diets reduce the risk for some adult type cancers such as breast cancer and prostate cancer. These studies form the basis of the American Cancer Society (ACS) nutritional guidelines. Many CAM diets prescribe a similar low fat, high fiber, high fruit and vegetable type diet, but also add detoxification and many different supplements to the basic diet which is then claimed to cure cancer. The potential advantages and disadvantages of CAM diets are discussed. Many aspects can be potentially harmful, particularly to the child with cancer. Advantages include involvement of the child and family in decision-making and care. There is no evidence to support the claims that CAM dietary therapies cure 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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.197
GPT teacher head0.463
Teacher spread0.266 · 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
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

Citations11
Published2007
Admission routes1
Has abstractyes

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