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Record W3097450343 · doi:10.1016/j.ctarc.2020.100233

Nutritional outcomes in head and neck cancer patients: is intensive nutritional care worth it?

2020· article· en· W3097450343 on OpenAlexaff
Sheilla de Oliveira Faria, Doris Howell, Marco Auré Vamondes Kulcsar, José Eluf‐Neto

Bibliographic record

VenueCancer Treatment and Research Communications · 2020
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsOntario Institute for Cancer ResearchUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsHead and neck cancerMedicineHead and neckIntensive care medicineHead (geology)CancerGerontologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: This study aimed to compare nutritional outcomes before and after implementation of weekly dietetic counseling (intensive nutritional care) in head and neck cancers patients. METHODS: A retrospective study with all head and neck patients, who received radiotherapy between January 2010 and December 2017 were performed. The main outcome was significant weight loss. Compliance to caloric and protein recommendations were also evaluated. RESULTS: In all, 472 patients were included. Weight loss was not different between before and after implementation (-6.7%; IQ -10.5/-1.9 vs -5.0%; IQ -9.8/-0.7;p=0.06).There were no significant difference in terms of meeting the recommended intake. Higher baseline body mass index and oral nutritional support predicted significant weight loss. CONCLUSION: Implementation of intensive nutritional care did not have an impact on weight loss and energy and protein intake in head and neck cancer patients. Further research would be of value to determine the appropriate service-delivery model to achieve optimal patient outcomes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.410

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 integrity0.0000.000
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 head0.257
GPT teacher head0.504
Teacher spread0.247 · 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 designObservational
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

Citations12
Published2020
Admission routes1
Has abstractyes

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