Relationship between Peripheral Arterial Disease and Incident Disability among Elderly Japanese: the Tsurugaya Project
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".