The impact of wait time on patient outcomes in knee and hip replacement surgery: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Total hip and total knee replacement surgery are in high demand, leading to long wait times for many patients. While on the waiting list, patients may experience worsening pain, reduced mobility, and deteriorating health. Given that long wait times are common for lower joint replacement surgery, it is important to understand how patient health changes during the wait period and whether this impacts patient outcomes after surgery. The aim of this scoping review will be to identify and describe the evidence regarding the impact of wait time on patient outcomes for patients who undergo total knee and total hip replacement surgery. METHODS: This scoping review was designed with guidance from the Joanna Briggs Institute Manual for Evidence Synthesis, and results will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. EMBASE, Medline, PubMed, Scopus, CINAHL, and Cochrane electronic databases will be searched for English language articles published after 1999. Studies of adult patients with osteoarthritis undergoing primary knee or hip replacement surgery, which measure patient outcomes over the wait period for surgery, will be included. Two independent reviewers will screen titles and abstracts followed by full article review. Data will be extracted by two reviewers using a standardized form. Outcomes assessed during the wait period will be identified and described in tables. Factors associated with changes in health status during the wait period will be qualitatively described. DISCUSSION: This review will map the evidence regarding wait times for lower extremity joint replacement surgery. Better understanding of how the impact of wait times on patient health status is measured over the perioperative period will inform future research on wait times. SCOPING REVIEW REGISTRATION: Registered with Open Science Framework, Feb 14, 2021 DOI: https://doi.org/10.17605/OSF.IO/MV4FS.
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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 | 0.093 | 0.087 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.068 | 0.013 |
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".