Risk factors for revision of total knee arthroplasty: a scoping review
Bibliographic record
Abstract
BACKGROUND: In spite of the increasing incidence of total knee arthroplasties (TKA), evidence is limited regarding risk factors for revision. The objective of this scoping review was to identify and assess demographic, surgical and health services factors that may increase the risk for revision surgery following TKA. METHODS: A scoping review was undertaken following an electronic search in MEDLINE (1990 to December 2013), CINAHL (to December 2013), EMBASE (1990 to December 2013) and Web of Science (1990 to December 2013). RESULTS: Of the 4460 articles screened, 42 were included of which 26 articles were based on registry data. Increased risk of revision was associated with demographic factors (younger age, African American), surgical factors related to the primary TKA (uncemented components, implant malalignment, increased surgery duration), and health services (low volume hospitals). CONCLUSIONS: Identifying emerging trends in characteristics of those requiring revision following TKA can help identify those at risk and allocate appropriate resources. Further primary clinical articles on risk factors for revision of TKA are necessary to ensure maximal function and lifespan following TKAs.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".