Predictors of low self-rated health in patients aged 65+ after total hip-replacement (THR) — A cross-sectional study
Bibliographic record
Abstract
Background: THR is as a very efficient operation in terms of pain-relief and improvement of walking ability. However, after the operation some patients still report low health status. Aim : The aim of the study is to describe health status among the patients following THR and to identify factors predicting low self-rated health after surgery. Material and method : A cross-sectional study including 287 patients aged 65+, who had had THR within 12-months were performed. Patients from five Danish counties received a mailed questionnaire assessing health status and demographic data. Short Form-36 measures eight domains of importance for health status. The measures are physical function, role physical, bodily pain, social function, role emotional, general health, vitality and mental health. Results: Patients living alone or being depend on help from others had a significantly increased risk of having lower scores in 7 of 8 domains of health status after surgery. Regression analysis revealed that living alone could predict significant lower score on two of the eight health domains. Conclusions: Our results indicate that health status is scored low in patients living alone or having no support. This implies that there might be a need for further postoperative interventions.
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 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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".