Relationship Between Kihon Checklist Score and Anxiety Levels in Elderly Patients Undergoing Early Phase II Cardiac Rehabilitation
Bibliographic record
Abstract
BACKGROUND: The frailty state consists of not only physical but also psycho-emotional problems, such as cognitive dysfunction and depression as well as social problems. However, few reports have examined the relationship between frailty and anxiety levels in elderly patients undergoing cardiac rehabilitation (CR). METHODS: We analyzed 255 patients (mean age: 74.9 ± 5.8 years, 67% male) who participated in early phase II CR at Juntendo University Hospital. At the beginning of CR, patients carried out self-assessments based on the Kihon Checklist (KCL) and the State Trait Anxiety Inventory Form (STAI). Patients were divided into three groups: frailty group (n = 99, 39%), pre-frailty group (n = 81, 32%), and non-frailty group (n = 75, 29%) according to the KCL. We assessed results from the KCL scores and its relationship with anxiety levels. RESULTS: Among the three groups, there were no significant differences in age, underlying illnesses, or the prevalence of coronary risk factors. Depressive mood domains of the KCL were significantly higher in the frailty and pre-frailty groups than in the non-frailty groups (3.0 ± 1.5 vs. 1.4 ± 1.2 vs. 0.4 ± 0.6; P < 0.01). The state anxiety level was significantly higher in the frailty group than in the non-frailty group (41.6 ± 0.9 vs. 34.9 ± 1.0; P < 0.01). The trait anxiety levels were significantly higher in the frailty group and pre-frailty group than in the non-frailty group (45.5 ± 0.9 vs. 39.2 ± 1.0 vs. 35.1 ± 1.1; P < 0.01). State anxiety and trait anxiety also showed a significantly positive correlations with the KCL scores (r = 0.32 vs. 0.41, P < 0.01). CONCLUSIONS: Frailty scores were positively correlated not only with physical function but also with depression mood and anxiety levels in elderly patients undergoing early phase II CR. These results suggest that assessment of depressive mood and anxiety is also important in elderly patients undergoing early phase II CR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".