Total knee arthroplasty for treatment of rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Rheumatoid arthritis (RA) is a very tricky orthopedic condition. If it can not be treated fairly well, it may greatly affect quality of life in patients with RA, and even can cause disability. Total knee arthroplasty (TKA) has reported to treat patients with RA effectively. However, no study has systematically explored its efficacy and complications for patients with RA. METHODS: Seven databases will be searched from their inceptions to the present without any language restrictions: MEDICINE, EMBASE, Cochrane Library, Web of Science, Allied and Complementary Medicine Database, Chinese Biomedical Literature Database, and China National Knowledge Infrastructure. Two authors will carry out all study selection, data extraction, and risk of bias assessment independently. RESULTS: The primary outcome of joint pain will be measured by any pain scales, such as visual analogue scale. The secondary outcomes will include joint function, quality of life, and postoperative adverse events. The joint function will be measured by The Western Ontario and McMaster Universities Arthritis Index, Knee Injury and Osteoarthritis Outcome Score, or other relevant scales. The quality of life will be assessed by the 36-Item Short Form Health Survey or any related tools. In addition, postoperative adverse events will also be analyzed. CONCLUSIONS: The findings of this study will summarize the latest existing evidence on the efficacy and safety of TKA for patients with RA. ETHICS AND DISSEMINATION: This study does not need ethical approval, because it will not analyze individual data. The results of this study are expected to be disseminated at peer-reviewed journals. PROSPERO REGISTRATION NUMBER: PROSPERO CRD42019133274.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".