Factors Associated With Psycho-Cognitive Functions in Patients With Persistent Pain After Surgery for Femoral Neck Fracture
Bibliographic record
Abstract
BACKGROUND: The aim of the study was to address issues arising from fracture of the femoral neck in elderly individuals, the prevalence of which continues to increase in Japan. The prevalence is increasing in Japan and there have been many reports on physical functions such as prevention of a fall. However, there have been a few studies that focus on psycho-cognitive functions. We must examine factors in patients with fractured femur necks to develop methods to assist affected patients. The current study aimed to examine factors associated with psycho-cognitive functions after surgery for fractured femoral neck in the Japanese elderly. METHODS: In this study, we examined the relationships among sex, age, fracture site, operative procedure, body mass index, lifestyle, psycho-cognitive functions, and types of pain in 142 patients, performed multiple regression analysis using the mini-mental state examination (MMSE) and the Montgomery-Asberg depression rating scale (MADRS) scores as dependent variables, and created MMSE and MADRS models. RESULTS: Analysis of MMSE and MADRS models identified night pain and the number of family members as factors that affected mental function in a population with persistent pain for 1 week after surgery for fractured femoral neck. In addition, the number of family members was identified in multiple regression analysis models as a factor associated with psycho-cognitive functions. Pain, and night pain in particular, affect psycho-cognitive functions. CONCLUSIONS: We speculated that emotional changes were associated with number of family members. Patients living with family members maintained psycho-cognitive functions better than did those living alone, even when they experienced pain in their daily lives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".