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Record W2327733285 · doi:10.1002/hed.24427

Recommended European Society of Parenteral and Enteral Nutrition protein and energy intakes and weight loss in patients with head and neck cancer

2016· review· en· W2327733285 on OpenAlexaff
Kaitlin H. Giles, Catherine Kubrak, Vickie E. Baracos, Kärin Olson, Vera C. Mazurak

Bibliographic record

VenueHead & Neck · 2016
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsAlberta Health ServicesAlberta HealthUniversity of Alberta
Fundersnot available
KeywordsMedicineWeight lossHead and neck cancerParenteral nutritionCancerEnteral administrationWeight changeClinical nutritionHead and neckInternal medicineSurgeryObesity

Abstract

fetched live from OpenAlex

BACKGROUND: Information regarding attenuation of weight loss in patients with head and neck cancer consuming energy and protein intakes at levels recommended by the European Society of Parenteral and Enteral Nutrition (ESPEN) is limited. METHODS: Newly diagnosed patients with head and neck cancer (n = 38) consuming food orally had weight and 3-day diet records prospectively collected at baseline, the end of treatment, and at the 2.5-month follow-up. Weight loss of patients consuming the ESPEN recommendations of ≥30 kcal/kg/d energy and 1.2 g/kg/d protein versus those consuming less were compared. Weight loss of oral nutrition supplement consumers versus oral nutrition supplement nonconsumers was also compared. RESULTS: Despite ≥30 kcal/kg/d intakes at posttreatment and follow-up, mean weight loss was 10.3% from baseline to posttreatment, and 4.0% from posttreatment to follow-up. At posttreatment, oral nutrition supplement consumers with intakes ≥30 kcal/kg/d lost twice as much weight as nonconsumers with intakes of ≥30 kcal/kg/d (p = .001). CONCLUSION: Current ESPEN recommendations may not attenuate weight loss in patients with head and neck cancer, especially those consuming oral nutrition supplements. © 2016 Wiley Periodicals, Inc. Head Neck 38:1248-1257, 2016.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.826
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.039
GPT teacher head0.335
Teacher spread0.296 · 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 designOther design
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

Citations43
Published2016
Admission routes1
Has abstractyes

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