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Record W2095409593 · doi:10.2310/7200.2007.009

Patients Previously Treated for Lymphoma Consume Inadequate or Excessive Amounts of Five Key Nutrients

2007· article· en· W2095409593 on OpenAlexvenueno aff
Nancy Russell, Deanna M. Hoelscher, Nicki Lowenstein

Bibliographic record

VenueJournal of the Society for Integrative Oncology · 2007
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineNutrientMicronutrientEnvironmental healthVitaminCancerVitamin D and neurologyGerontologyInternal medicinePathologyBiology

Abstract

fetched live from OpenAlex

Adequate amounts of nutrients such as folate, vitamin A, iron, selenium and calcium are essential for general health including prevention of cancer. Yet, excess amounts of vitamin A, folate, and iron may also promote cancer. This study sought to determine whether adults who had completed initial treatments for B-cell lymphoma from 1 to 3 years earlier were consuming recommended amounts of these key nutrients and their interests in nutritional education. We surveyed 141 patients undergoing follow-up in the Lymphoma/Myeloma Clinic at The University of Texas M. D. Anderson Cancer Center using a validated food frequency questionnaire and supplemental questionnaire regarding nutritional interest. Nutrient intakes were estimated based on national databases of average content in foods and compared with recommended guidelines. One hundred forty-one participants returned complete questionnaires, but errors limited some nutrient estimates to 134 participants. Participants' mean age was 50, 55% were male, and 80% were non-Hispanic whites. Most participants (94%) were consuming either inadequate or excessive amounts of one or more of these key nutrients. Half of the participants were interested in receiving nutritional education. These findings are of concern because of their potential impact upon recovery and maintenance of general health and possibly cancer-related pathways after treatment.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.039
GPT teacher head0.385
Teacher spread0.346 · 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 designNot applicable
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

Citations4
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Society for Integrative OncologySame topicNutrition and Health in AgingFrench-language works237,207