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Record W2148568574 · doi:10.1093/geront/gns050

Clinical Diagnoses Before Age 75 and Men's Survival to Their 85th Birthday: The Manitoba Follow-up Study

2012· article· en· W2148568574 on OpenAlexafffundabout
Robert B. Tate, L Michaels, T. Edward Cuddy, Dennis J. Bayomi

Bibliographic record

VenueThe Gerontologist · 2012
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health ResearchUniversity of British Columbia
KeywordsGerontologyMedical diagnosisDemographyMedicineHistorySociologyPathology

Abstract

fetched live from OpenAlex

PURPOSE: Of all Canadian and American men who live to age 75 years, about half can expect to live to age 85. Our objective is to examine how clinical diagnoses made before age 75 relate to a man's survival to age 85 years. DESIGN AND METHODS: Since 1948, a cohort of 3,983 young men (mean age of 31 years at entry) has been followed with routine contact and medical examinations to prospectively document incident disease. Over 62 years of follow-up, 2,414 of the cohort lived to celebrate their 75th birthday. Of these survivors, 1,060 (44%) died before their 85th birthday. Cox proportional hazard models were used to examine the effects of ischemic heart disease, cancer, cerebrovascular disease, diabetes mellitus, peripheral arterial disease, and chronic obstructive pulmonary disease on all-cause mortality between age 75 and 85 years. RESULTS: Modeled as six binary risk factors at age 75 years, all were significantly (p < .01) and independently related to 10-year mortality. Multivariate risk ratios ranged from 1.36 to 1.46 except for chronic obstructive pulmonary disease with a risk ratio of 1.85 (95% CI: 1.38, 2.49). The cumulative 10-year probability of survival from age 75 to 85 among men with none of these diagnoses was 63%, 52% for any one diagnosis, 39% for two diagnoses, and 22% for three or more diagnoses. IMPLICATIONS: Joint independence of these six common clinical diagnoses implies that each is important and their effects on mortality are cumulative.

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)0.002
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 score0.042
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.135
GPT teacher head0.392
Teacher spread0.257 · 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

Citations7
Published2012
Admission routes3
Has abstractyes

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