MétaCan
Menu
Back to cohort
Record W2736842615 · doi:10.1111/ggi.13112

Predicted factors for older Taiwanese to be healthy octogenarians: Results of an 18‐year national cohort study

2017· article· en· W2736842615 on OpenAlexfundno aff
Wei‐Chung Hsu, Alan C. Tsai, Yu‐Chia Chen, Jiun‐Yi Wang

Bibliographic record

VenueGeriatrics and gerontology international/Geriatrics & gerontology international · 2017
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersHealth Promotion Administration, Ministry of Health and WelfareAGE-WELL
KeywordsMedicineLogistic regressionGerontologyCohortSuccessful agingHealthy agingCohort studyLongitudinal studyActivities of daily livingDemographyPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

AIM: To identify factors that predict the 62-69 years old Taiwanese to be healthy octogenarians. METHODS: We analyzed the 1989 (baseline), and 2003 and 2007 (end-point) datasets of the Taiwan Longitudinal Survey on Aging, a national cohort study. A total of 1977 participants aged 62-69 years at baseline were tracked for 14-18 years. The outcome measure was "being healthy octogenarians", defined as participants who were aged ≥80 years, free from activities of daily living dependency, depressive symptoms or cognitive impairment, and able to provide social support. A logistic regression model was used to identify the predictors. RESULTS: The results showed that higher educational level, conjugal living, absence of smoking or betel quid chewing, moderate alcohol drinking, routine physical activity, more leisure activities, no hypertension, no diabetes, sleeping well and satisfied with economic condition were the positive predictors for becoming a healthy octogenarian. CONCLUSIONS: Using a multidimensional criterion, the present study identified a list of factors in predicting older Taiwanese becoming healthy octogenarians. The findings highlight the need to identify potential factors for various populations. Many of the predictors are modifiable factors. The present results would be valuable for planning effective health promotion strategies to achieve healthy aging for older adults. Geriatr Gerontol Int 2017; 17: 2579-2585.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0010.000
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 head0.066
GPT teacher head0.378
Teacher spread0.312 · 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.

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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueGeriatrics and gerontology international/Geriatrics & gerontology internationalSame topicFrailty in Older AdultsFrench-language works237,207