Association between ongoing pain intensity, health-related quality of life, disability and quality of sleep in elderly people with total knee arthroplasty
Bibliographic record
Abstract
The scope of this paper was to study the relationship between pain intensity, health-related quality of life, disability, sleep quality and demographic data in elderly people with total knee arthroplasty (TKA). 24 subjects who had been subjected to TKA the previous month (4 females; 66 ± 9years) and 21 comparable controls (8 male; 70 ± 9years) participated in the study. Intensity of pain, and highest and lowest pain intensity experienced in the preceding week were collected. The Western Ontario and McMaster Universities index function, quality of life (Medical Outcomes Study Short Form 36), and Pittsburgh Sleep Quality Index were assessed. Age, gender, weight, height, body mass index were also collected. Individuals with TKA presented worse physical function (P < 0.01), social role (P = 0.01), physical performance (P < 0.01), pain (P = 0.04), disability (P = 0.04) and sleep quality (P = 0.03) than the controls. Higher intensity of pain was associated with lower physical function, social role, mental health, vitality and general health, and with higher disability and sleep quality. Disability and sleep quality were negatively associated with several quality of life domains. The associations between the intensity of pain, disability, quality of life and sleep reveal the multidimensional experience of TKA.
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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 | 0.000 |
| 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 | 0.000 |
| 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".