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Record W3201530858 · doi:10.1007/978-3-030-63892-4_13

Multidisciplinary Nutrition Care: Benefitting Patients with Malnutrition Across Healthcare Sectors

2021· book-chapter· en· W3201530858 on OpenAlexaff
Heather Keller, Susan E. Slaughter, Leah Gramlich, Ashwini Namasivayam‐MacDonald, Jack Bell

Bibliographic record

VenuePerspectives in nursing management and care for older adults · 2021
Typebook-chapter
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of AlbertaResearch Institute for AgingMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsMultidisciplinary approachMalnutritionHealth careMedicineNursingPolitical science

Abstract

fetched live from OpenAlex

Abstract Geriatric malnutrition prevention, detection, and treatment benefit from a multidisciplinary approach, regardless of the care setting. Nutrition care pathways have been created to support multidisciplinary care for hospitals and for transitions and primary care. Conceptual models for supporting nutrition in long-term care emphasize a multidisciplinary approach.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.013
GPT teacher head0.317
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations8
Published2021
Admission routes1
Has abstractyes

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