Effectiveness of Telemedicine on Wound-Related and Patient-Reported Outcomes in Patients With Chronic Wounds: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Telemedicine may provide new vitality and opportunities to the field of wound care and has been advocated as being a potential and feasible strategy for chronic wound management. Objective: This systematic review and meta-analysis aimed to assess the effectiveness of telemedicine on wound-related outcomes and patient-reported outcomes in patients with chronic wounds. Methods: A comprehensive search of 9 databases, including PubMed, Embase, PsycINFO, the Cochrane Library, CINAHL, Web of Science, the China National Knowledge Infrastructure database, the Wanfang database, and the VIP database, was performed to identify eligible randomized controlled trials that investigated the effectiveness of telemedicine for patients with chronic wounds. The primary outcome was wound healing, including healing score, healing time, and healing rate. The quality of the included studies was examined via the Cochrane risk-of-bias tool. Data synthesis was conducted via Review Manager (version 5.4; the Cochrane Collaboration). Due to anticipated heterogeneity, a random-effects meta-analysis was used. Effect estimates are presented as risk ratio (RR) or standard mean differences (SMDs) with 95% CI. The quality of the evidence was assessed via the Grading of Recommendations, Assessment, Development, and Evaluation approach. Results: A total of 22 randomized controlled trials involving 2397 participants met the inclusion criteria. This review demonstrated that telemedicine significantly improved the healing score (SMD -1.46, 95% CI -2.27 to -0.66; P<.001; I2=95%; P<.001), healing time (SMD -0.47, 95% CI -0.92 to 0.02; P=.04; I2=85%; P<.001), amputation rate (RR 0.52, 95% CI 0.31-0.88; P=.02; I2=23%; P=.28), pain (SMD-0.62, 95% CI -0.90 to -0.34; P<.001; I2=0%; P=.32), and quality of life (SMD 1.90, 95% CI 0.32-3.48; P=.02; I2=98%; P<.001). Although the meta-analysis results indicated that telemedicine enhanced the healing rate (RR 1.16, 95% CI 1.02-1.33; P=.03; I2=50%; P=.03), potential publication bias was detected (Egger test, bias=1.801; SE 0.367; P<.001). Upon imputing the missing studies using the trim-and-fill method, the recalculated pooled RR was adjusted, resulting in a new estimate of RR 1.06 (95% CI 0.98-1.15; P=.16). In addition, no significant differences were found in mortality, depression, anxiety, or patient satisfaction. Conclusions: There is some evidence that telemedicine contributes to improvements in the healing score, healing time, amputation rate, pain, and quality of life of patients with chronic wounds. Nevertheless, further high-quality studies are essential to examine the impact of telemedicine on healing rate and patient-reported outcomes in patients with chronic wounds.
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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.038 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".