Strategies to Manage Poorer Outcomes After Hip or Knee Arthroplasty: A Narrative Review of Current Understanding, Unanswered Questions, and Future Directions
Bibliographic record
Abstract
PURPOSE: Although hip or knee arthroplasty is generally a successful intervention, it is documented that 15%-30% of patients undergoing arthroplasty report suboptimal outcomes. This narrative review aims to provide an overview of the key findings concerning the management of poorer outcomes after hip or knee arthroplasty. METHOD: A comprehensive search of articles was conducted up to November 2023 across three electronic databases. Only studies written in English were included, with no limitations applied regarding study design and time. RESULT: Efficiently addressing poorer outcomes after arthroplasty necessitates a thorough exploration of appropriate methods for assessing recovery following hip or knee arthroplasty, ensuring accurate identification of patients at risk or experiencing poorer recovery. When selecting appropriate outcome measure tools, various factors should be taken into consideration, including understanding patients' priorities throughout the recovery process, assessing psychometric properties of outcome measure tools at different time points after arthroplasty, understanding how to combine/reconcile provider-assessed and patient-reported outcome measures, and determining the appropriate methods to interpret outcome measure scores. However, further research in these areas is warranted. In addition, the identification of key modifiable factors affecting outcomes and the development of interventions to manage these factors are needed. CONCLUSION: There is growing attention paid to delivering interventions for patients at risk or not optimally recovering following hip or knee arthroplasty. To achieve this, it is essential to identify the most appropriate outcome measure tools, factors associated with poorer recovery and management of these factors.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".