Effectiveness of eHealth Tools for Hip and Knee Arthroplasty: A Systematic Review
Bibliographic record
Abstract
Objective: This study aimed to compare the effectiveness and costs of eHealth tools with usual care in delivering health-related education to patients' undergoing total hip or knee arthroplasty due to osteoarthritis. Data Sources: Six electronic databases were searched to identify randomized controlled trials and experimental designs (randomized or not) examining the effect of eHealth tools on pre- or post-operative care. Only manuscripts written in English were included. In the current study, no specific primary or secondary outcomes were selected. Any study that investigated the impacts of eHealth tools on hip or knee arthroplasty outcomes were included. Review Methods: Two researchers reviewed all titles and abstracts independently and in duplicate. Two researchers also conducted full-text screening and data extraction from the 26 selected articles. Results: The data were descriptively reported, and themes could emerge from each outcome. Two researchers separately assessed the Risk of Bias for each paper using the Cochrane risk of bias assessment tool. The majority of studies evaluated the impact of eHealth tools on physical ( n = 23) and psychosocial outcomes ( n = 19). Cost-related outcomes were measured in 7 studies. eHealth tools were found to be equivocal to usual care, with few studies reporting statistically significant differences in physical or psychosocial outcome measures. However, cost-related outcomes showed that using eHealth tools is more cost-effective than usual care. Conclusions: This review demonstrated that eHealth tools might be as effective as usual care, and possibly more cost-effective, a crucial implication for many overly burdened health care systems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".