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Factors related to improvement and decline in high and low nutritional risk community‐dwelling Canadian older men: The Manitoba Follow‐up Study (MFUS) (1026.8)

2014· article· en· W1529972818 on OpenAlexafffundabout
Christina Lengyel, Elisabeth Harms, Robert B. Tate

Bibliographic record

VenueThe FASEB Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health ResearchManitoba Health Research Council
KeywordsGerontologyMedicinePsychological interventionRespondentEnvironmental healthDemographyOverweightObesityInternal medicine

Abstract

fetched live from OpenAlex

Nutritional risk screening is used to identify older adults who may be at increased risk for poor nutritional intake or impaired status. The objectives of this study were to: 1) determine if nutritional risk can change over time in community‐dwelling Canadian older men; 2) examine the components of SCREEN II (Seniors in the Community: Risk Evaluation for Eating and Nutrition) that improve among men at high nutritional risk; and 3) examine the components of SCREEN II that decline among men at low nutritional risk. In 2007, 690 MFUS ( www.mfus.ca ) members were mailed the SCREEN II survey (80% completion rate; mean age = 86.7 years). The survey has been sent annually with up to 5 surveys received from each 2007 respondent, enabling a longitudinal assessment of change in factors among high and low nutritional risk men. Across one year transition periods, 19% of men at high nutritional risk improved their status, 20% of men at low nutritional risk declined, while 61% had no change. Decreased appetite and dietary intake (milk products, meat & alternatives, fluid, fruits and vegetables) were the most common changes for men at low nutritional risk showing decline compared to intentional weight change in men at high risk showing improvement. As factors related to improvement and decline of nutritional risk for community‐dwelling older men are different, targeted screening, follow‐up and strategic interventions are critical. Grant Funding Source : Supported by CIHR, MHRC, Dr. Paul H.T Thorlakson Foundation & University of Manitoba Centre on Aging

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.027
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0030.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.293
Teacher spread0.260 · 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 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

Citations0
Published2014
Admission routes3
Has abstractyes

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