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Record W4317830496 · doi:10.1080/01635581.2023.2170431

Assessing Impact of Nutrition Care by Registered Dietitian Nutritionists on Patient Medical and Treatment Outcomes in Outpatient Cancer Clinics: A Cohort Feasibility Study

2023· article· en· W4317830496 on OpenAlexaff
Dolores D. Guest, Tricia Cox, Anne Coble Voss, Kathryn Kelley, Xingya Ma, Andreea Nguyen, Kerry McMillen, Valaree Williams, James A. Lee, Jennifer L. Petersen, Karilynne Lenning, Elizabeth Yakes Jimenez

Bibliographic record

VenueNutrition and Cancer · 2023
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
FundersNational Center for Advancing Translational SciencesNational Cancer InstituteAcademy of Nutrition and Dietetics
KeywordsMedicineMalnutritionCohortMedical recordEmergency medicineOutpatient clinicCohort studyEmergency departmentPediatricsInternal medicineNursing

Abstract

fetched live from OpenAlex

More information is needed about the impact of outpatient nutrition care from a registered dietitian nutritionist (RDN) on patient outcomes. This study aimed to assess the feasibility of a cohort study design to evaluate impact of RDN nutrition care on patient outcomes, describe clinic malnutrition screening practices, and estimate statistical parameters for a larger study. Seventy-seven patients with lung, esophageal, colon, rectal, or pancreatic cancer from six facilities were included (41 received RDN care and 36 did not). RDN nutrition care was prospectively documented for six months and documented emergency room visits, unplanned hospitalizations and treatment changes were retrospectively abstracted from medical records. Most facilities used the Malnutrition Screening Tool (MST) to determine malnutrition risk. Patients receiving RDN care had, on average, five, half hour visits and had more severe disease and higher initial malnutrition risk, although this varied across sites. Documented medical and treatment outcomes were relatively rare and similar between groups. Estimated sample size requirements varied from 113 to 5856, depending on tumor type and outcome, and intracluster correlation coefficients (ICCs) ranged from 0 to 0.47. Overall, the methods used in this study are feasible but an interventional or implementation design might be advantageous for a larger study.

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 score0.029
Threshold uncertainty score0.730

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 head0.109
GPT teacher head0.486
Teacher spread0.377 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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