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Record W2071264308 · doi:10.5737/1181912x122102107

Nutritional support of the patient receiving high-dose therapy with hematopoietic stem cell support

2002· article· en· W2071264308 on OpenAlexaffvenueabout
Sheryl McDiarmid

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2002
Typearticle
Languageen
FieldMedicine
TopicOral health in cancer treatment
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineMucositisMalnutritionIntensive care medicineParenteral nutritionHematopoietic stem cell transplantationPopulationNauseaAdverse effectPsychological interventionAnorexiaVomitingTransplantationPediatricsSurgeryInternal medicineRadiation therapyNursingEnvironmental health

Abstract

fetched live from OpenAlex

Hematopoietic stem cell transplantation (HSCT) is an intensive therapy that is being used increasingly in an attempt to cure certain malignancies. One of the major adverse effects of this treatment is an inadequate oral intake that may result in dehydration and malnutrition. Factors that may contribute to inadequate oral intake include mucositis, nausea, vomiting, and anorexia. In addition, prior to transplant, many patients may have experienced, or continue to experience malnutrition associated with malignancy and its therapy. Traditionally, total parenteral nutrition (TPN) has been the mainstay of nutritional support in this patient population. The blood and marrow transplant (BMT) team at the Ottawa Hospital has significantly decreased the use of TPN through the initiation of a comprehensive nutritional support program that uses a variety of interventions including oral supplementation and enteral feeding. Understanding the causes and implications of malnutrition, and using tools that allow risk assessment and timely implementation of appropriate nutritional interventions, may facilitate full patient recovery parallel to hematopoietic recovery in the HSCT patient population.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score0.999

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.299
Teacher spread0.264 · 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.

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

Citations14
Published2002
Admission routes3
Has abstractyes

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