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Record W2992941272 · doi:10.5770/cgj.22.391

Self-Rated Health Predicts Mortality in Very Old Men—the Manitoba Follow-Up Study

2019· article· en· W2992941272 on OpenAlexafffundvenueabout
Christian R. Hanson, Philip D. St. John, Robert B. Tate

Bibliographic record

VenueCanadian Geriatrics Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Manitoba
FundersMax Rady College of Medicine, University of ManitobaUniversity of Manitoba
KeywordsMedicineDemographySelf-rated healthHazard ratioProportional hazards modelGerontologyProspective cohort studyCohort studyConfidence intervalSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Self-rated health (SRH) predicts death, but there are few studies over long-time horizons that are able to explore the effect age may have on the relationship between SRH and mortality. OBJECTIVES: 1. To determine how SRH evolves over 20 years; and 2. To determine if SRH predicts death in very old men. METHODS: We analyzed a prospective cohort study of men who were fit for air crew training in the Second World War. In 1996, a regular questionnaire was administered to the 1,779 surviving participants. SRH was elicited with a 5-point Likert Scale with the categories: excellent, very good, good, fair and poor/bad. We examined the age-specific distribution of SRH in these categories from the age of 75 to 95 years, to the end of the follow-up period in 2018. We constructed age-specific Cox proportional hazard models with an outcome of time to death. RESULTS: SRH declined with age. The gradient in risk of death persisted across all ages; those with poor/fair/bad SRH had consistently higher mortality rates. However, the discrimination between good and excellent was less in those aged 85+. CONCLUSIONS: SRH declines with advancing age, but continues to predict death in older men.

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)0.005
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 score0.161
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.031
GPT teacher head0.310
Teacher spread0.279 · 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

Citations2
Published2019
Admission routes4
Has abstractyes

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