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Record W2114108053 · doi:10.1093/ageing/afr176

The impact of social vulnerability on the survival of the fittest older adults

2012· article· en· W2114108053 on OpenAlexafffundabout
Melissa K. Andrew, A. Mitnitski, Susan Kirkland, Kenneth Rockwood

Bibliographic record

VenueAge and Ageing · 2012
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsDalhousie University
FundersCanadian Institutes of Health Research
KeywordsMedicineVulnerability (computing)GerontologySurvival of the fittestDemographyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: even older adults who are fit experience adverse health outcomes; understanding their risks for adverse outcomes may offer insight into ambient population health. Here, we evaluated mortality risk in relation to social vulnerability among the fittest older adults in a representative community-dwelling sample of older Canadians. METHODS: in this secondary analysis of the Canadian Study of Health and Aging, participants (n = 5,703) were aged 70+ years at baseline. A frailty index was used to grade relative levels of fitness/frailty, using 31 self-reported health deficits. The analysis was limited to the fittest people (those reporting 0-1 health deficit). Social vulnerability was trichotomised from a social vulnerability scale, which consisted of 40 self-reported social deficits. RESULTS: five hundred and eighty-four individuals had 0-1 health deficit. Among them, absolute mortality risk rose with increasing social vulnerability. In those with the lowest level of social vulnerability, 5-year mortality was 10.8%, compared with 32.5% for those with the highest social vulnerability (adjusted hazard ratio 2.5, 95% CI: 1.5-4.3, P = 0.001). CONCLUSIONS: a 22% absolute mortality difference in the fittest older adults is of considerable clinical and public health importance. Routine assessment of social vulnerability by clinicians could have value in predicting the risk of adverse health outcomes in older adults.

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)0.001
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 score0.307
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.022
GPT teacher head0.302
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

Citations107
Published2012
Admission routes3
Has abstractyes

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