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Record W4405964708 · doi:10.1093/geroni/igae098.2403

SELF-RATED HEATH PREDICTS INCIDENT MODERATE/SEVERE DISABILITY IN AGING MEN: THE MANITOBA FOLLOW-UP STUDY

2024· article· en· W4405964708 on OpenAlexaffabout
Phil St. John, Scott Nowicki, Robert B. Tate

Bibliographic record

VenueInnovation in Aging · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Wellbeing Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsGerontologyPsychologyMedicineDemographySociology

Abstract

fetched live from OpenAlex

Abstract Background There is evidence that Self-Rated Health (SRH) predicts death, but less evidence that SRH predicts new disability. Objective: To determine if SRH predicts moderate/severe disability in a prospective cohort study of aging men. Methods We conducted an analysis of a prospective cohort study of men who qualified for air crew training in the Royal Canadian Air Force, which began in 1948. Since 2004, an annual survey is sent out which measures age-reference SRH, functional status, and the Short-Form-36 (SF-36). In 2004, 796 men were alive, responded to the questionnaire and did not have disability. Functional status has been measured with an annual questionnaire which includes items on Basic Activities of Daily Living (BADLs) and Instrumental ADLs (IADLs). We considered impairment to be two or more BADLs impaired (of five considered) or three or more impaired IADLs (of seven). We calculated the median time to disability, and compared groups with a log rank test. We then constructed a proportional hazards model for the outcome of new disability. Results SRH was associated with incident disability: Compared to those with Excellent SRH, the Hazard Ratio (95% confidence interval) was 1.79 (1.19, 2.68) for those with Very Good SRH; 2.87 (1.91, 4.30) for those with Good SRH; and 2.40 (2.04, 5.65) for those with Fair/Poor/Bad SRH. This association persisted after adjusting for age, the SF-36 and chronic diseases. Conclusions: SRH predicts new disability.

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)0.009
metaresearch head score (Gemma)0.001
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 score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.072
GPT teacher head0.438
Teacher spread0.366 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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