Patients Previously Treated for Lymphoma Consume Inadequate or Excessive Amounts of Five Key Nutrients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".