The Promise of Smartphone Applications in the Remote Monitoring of Postsurgical Wounds: A Literature Review
Bibliographic record
Abstract
OBJECTIVE: To review the clinical and scientific literature on remote monitoring and management of postsurgical wounds using smartphone applications (apps). DATA SOURCES: MEDLINE, PubMed, EMBASE, and Cochrane libraries were searched for relevant articles on patients who received surgery and were monitored postdischarge via an app. STUDY SELECTION: Articles were selected with the terms "mobile phones," "smartphones," "wounds," "monitor," and "patient preference." DATA EXTRACTION: The authors found 276 review articles related to telemedicine in wound care. Investigators reviewed the titles and abstracts of the search results and selected 83 articles that were relevant to the remote monitoring of wounds using smartphone apps. DATA SYNTHESIS: The topics explored in selected literature included smartphone app importance to telemedicine, benefits (medical and financial), app examples, and challenges in the context of wound monitoring and management. The authors identified several challenges and limitations that future studies in the field need to address. CONCLUSIONS: Remote monitoring and management of wounds using smartphone apps is a valuable technique to enhance the quality of and access to healthcare. However, although some patients may prefer this technology, some lack technological competence, limiting telemedicine's applicability. In addition, issues remain with the reliable interpretation of data collected through apps.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".