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Record W1990094895 · doi:10.1080/21551197.2013.840256

Nutritional Risk and 5-Year Mortality of Older Community-Dwelling Canadian Men: The Manitoba Follow-Up Study

2013· article· en· W1990094895 on OpenAlexafffundabout
Valerie E. Broeska, Christina Lengyel, Robert B. Tate

Bibliographic record

VenueJournal of Nutrition in Gerontology and Geriatrics · 2013
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsManitoba HealthUniversity of Manitoba
FundersCanadian Institutes of Health Research
KeywordsMedicineHazard ratioGerontologyDemographyPsychological interventionPercentileCohortCohort studyRisk assessmentMortality rateEnvironmental healthConfidence intervalSurgeryInternal medicine

Abstract

fetched live from OpenAlex

This study examines nutritional risk and 5-year mortality rates of community-dwelling older Canadian men participating in the Manitoba Follow-Up Study. The surviving cohort (n = 690; mean age = 86.8 years) was mailed a self-administered Nutrition Survey consisting of SCREEN II, a validated nutrition risk screening tool and health-related questions. Five hundred fifty-three completed surveys (80% completion rate) were returned, with 522 (94%) containing complete responses necessary to score nutritional risk, comprising the participants for this analysis. Forty-four percent of the 522 respondents were scored as high risk, 24% at moderate risk, and 32% at low risk. Over five years from 2007 to 2012, 212 (41%) of the men died, with 1-year, 2-year, 3-year, and 4-year survival rates of 92%, 86%, 77%, and 66%, respectively. Men in the lowest 40th percentile of the nutritional risk distribution accounted for half of all deaths. Adjusted for other characteristics, Cox proportional hazard models demonstrated that with each unit decline on the nutritional risk scale there was a 4% greater risk of mortality (hazard ratio = 0.96 [95% CI 0.94,0.98]). Early identification of older men at nutritional risk and timely nutrition interventions are essential in delaying the progression of morbidity and mortality.

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)0.001
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 score0.026
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.052
GPT teacher head0.324
Teacher spread0.273 · 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

Citations19
Published2013
Admission routes3
Has abstractyes

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