Mobile Application's Effect on Patient Satisfaction and Compliance in Total Joint Arthroplasty: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Use of mobile applications to improve patient engagement is particularly promising in total joint arthroplasty (TJA) whereby successful outcomes are predicated by patient engagement. In accordance with published guidelines by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, studies were searched, screened, and appraised for quality on various search engines. Hedges' g or odds ratios of patient adherence were reported. Twelve studies met the inclusion criteria, and the average age of 9,521 patients included was 60 years. Six studies concluded that mobile applications improved patients' satisfaction, with Hedges' g revealing an effect size of 1.64 (95% confidence interval [CI] 0.90 to 2.37), P < 0.001, in favor of mobile applications increasing patient satisfaction. Six studies reported improvements in compliance demonstrating an odds ratio for improved adherence of 4.57 (95% CI, 1.66 to 12.62), P < 0.001. Two studies reported a reduction in unscheduled office or emergency department visits. With evolving reimbursement policies linked to outcomes paired with the exponentially increasing volume of TJA performed, innovative ways to efficiently deliver high-quality care are in demand. Our systematic review is limited by a dearth of research on the nascent technology, but the available data suggest that mobile applications may enhance patient satisfaction, improve compliance, and reduce unscheduled visits after TJA.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".