Dukungan Sosial pada Pasien Post Total Knee Replacement : Literature Riview
Bibliographic record
Abstract
Background: Ostheoatritis disease become triggers to do Total Knee Replacement (TKR). Patients TKR will experience pain, physical limitations and muscle stiffness that causes a quite long recovery. The effects quite long recovery can influence quality of life. It can be measured using WOMAC scale (The Western Ontario and McMaster Universities Osteoarthritis Index). Objective: Knowing the influence of social support on post-operation TKR patient recovery. Method: Searching relevant for data base literature sources using Ebsco, Pubmed, Sciencedirect, and Google Scholar search engine using Keywords: Social Support, Level Independence, Quality Of Life, Total Knee Replacement, from the searching Appropriate results with the inclusion and exclusion criteria, that can be eight articles include literature review where the article is taken to pressure quality of life which uses the WOMAC scale. Result : There are seven of Eight articles state social support can influence the quality of life to TKR recovery and one study haven’t Influence, because age, complication, and don’t live with family. Conclusion: Social support proven to influence health status and quality of life in TKR patients recovery.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".