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Record W2127557338 · doi:10.5551/jat.5389

Relationship between Peripheral Arterial Disease and Incident Disability among Elderly Japanese: the Tsurugaya Project

2010· article· en· W2127557338 on OpenAlexaff
Akemi Nitta, Atsushi Hozawa, Shinichi Kuriyama, Naoki Nakaya, Kaori Ohmori‐Matsuda, Toshimasa Sone, Masako Kakizaki, Satoru Ebihara, Masataka Ichiki, Hiroyuki Arai, Ichiro Tsuji

Bibliographic record

VenueJournal of Atherosclerosis and Thrombosis · 2010
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsMedicineHazard ratioConfoundingProportional hazards modelConfidence intervalPhysical disabilityArterial diseaseInternal medicinePeripheralPhysical therapyVascular disease

Abstract

fetched live from OpenAlex

AIM: The aim of this study was to investigate whether peripheral arterial disease (PAD) is predictive of disability and whether the relationship between PAD and disability can be fully explained by baseline physical functions. METHODS: We followed for five years 783 Japanese aged 70 years or older without a disability at baseline in 2003. We defined participants certificed as requiring long-term care as having incident disability. The hazard ratio (HR) and 95% confidence interval (95% CI) for incident disability were calculated using the Cox proportional hazards model. RESULTS: After adjusting for possible confounders other than physical function, the HR of incident disability among participants with PAD was 1.86 (95%CI: 1.06 to 3.26).Although the risk was attenuated (HR=1.63, 95%CI: 0.92 to 2.86) after adding baseline physical function as a covariate, the HR was still high. Furthermore, the relation was not statistically significant, but the group with higher physical function and PAD also had a higher HR of incident disability than those who had higher physical function without PAD. CONCLUSION: PAD is an important predictor of disability even if the level of baseline physical function is high.

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)0.000
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 score0.003
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.043
GPT teacher head0.303
Teacher spread0.260 · 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

Citations14
Published2010
Admission routes1
Has abstractyes

Explore more

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