Influence of frailty on postoperative outcomes following primary and revision total hip arthroplasty
Bibliographic record
Abstract
Total hip arthroplasty (THA) effectively treats advanced hip disorders, yet outcomes vary among patients. Frailty has become a crucial factor influencing these results. Several studies explored multiple preoperative factors affecting THA outcomes, highlighting the significance of age, Western Ontario and McMaster Universities Osteoarthritis Index, Center for Epidemiologic Studies Depression Scale, and central sensitization index scores in predicting post-operative recovery, emphasizing comprehensive preoperative assessments. Subsequent research has shown that frailty, measured by tools like the hospital frailty risk score and frailty deficit index, is significantly associated with adverse outcomes such as higher 30-day readmission rates, longer hospital stays, increased costs, and elevated mortality and complication risks in both primary and revision THA. Additionally, frailty related to short-term adverse events but stressed the need for standardized frailty measurement. Currently, there is no unified standard for assessing frailty before THA, which hinders cross-study comparison and evidence-based guideline development. Future research should focus on establishing a universal frailty assessment standard considering physical function, comorbidities, cognitive and psychological status. Prospective studies are also needed to clarify the causal relationship between frailty and long-term THA outcomes and identify modifiable factors for preoperative interventions. Overall, understanding the impact of frailty on THA outcomes is essential for improving patient care and resource utilization, especially in an aging population with a rising prevalence of hip disorders.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".