Disparities in Outcomes for African Americans and Whites Undergoing Total Knee Arthroplasty: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: African Americans in the United States undergo total knee arthroplasty (TKA) less often than whites, in part because of lower expectations among African Americans for successful surgery. Whether this lower expectation is justified is unknown. Our objective is to compare health-related quality of life (HRQOL) and satisfaction after TKA between African Americans and whites. METHODS: A systematic review of English language articles using Medline, the Cochrane register, Embase (April 21, 2015), and a hand search of unlisted disparities journals was performed. Search terms included total knee replacement, quality of life, outcomes, and satisfaction. High-quality cohort studies that examined HRQOL in African Americans and white adults 6 months or more after TKA were included. RESULTS: Of the 4781 studies screened by title, and 346 by abstract, 7 studies included race in their analysis. Results included 5570 TKA patients, 4077 whites (89%), and 482 (11%) blacks. Because studies used different outcome measures and were inconsistent in their adjustment for confounders, we could not perform a quantitative synthesis of results. In 5 studies, US blacks had worse pain, in 5 worse function, and in 1 less satisfaction 6 months to 2 years after TKA. CONCLUSION: US blacks may derive less benefit from TKA than whites as measured by HRQOL, pain, function, and satisfaction. Many studies assessing predictors of patient-related TKA outcomes fail to analyze race as a variable, which limited our study. More studies assessing the effect of race and socioeconomic factors on TKA outcome are needed.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| 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) | 0.004 | 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".