Using Telemedicine to Diagnose Surgical Site Infections in Low- and Middle-Income Countries: Systematic Review
Bibliographic record
Abstract
BACKGROUND: A high burden of preventable morbidity and mortality due to surgical site infections (SSIs) occurs in low- and middle-income countries (LMICs), and most of these SSIs occur following discharge. There is a high loss to follow-up due to a wide geographical spread of patients, and cost of travel can result in delayed and missed diagnoses. OBJECTIVE: This review analyzes the literature surrounding the use of telemedicine and assesses the feasibility of using mobile phone technology to both diagnose SSIs remotely in LMICs and to overcome social barriers. METHODS: A literature search was performed using Medline, Embase, CINAHL, PubMed, Web of Science, the Cochrane Central Register of Controlled Trials and Google Scholar. Included were English language papers reporting the use of telemedicine for detecting SSIs in comparison to the current practice of direct clinical diagnosis. Papers were excluded if infections were not due to surgical wounds, or if SSIs were not validated with in-person diagnosis. The primary outcome of this review was to review the feasibility of telemedicine for remote SSI detection. RESULTS: A total of 404 articles were screened and three studies were identified that reported on 2082 patients across three countries. All studies assessed the accuracy of remote diagnosis of SSIs using predetermined telephone questionnaires. In total, 44 SSIs were accurately detected using telemedicine and an additional 14 were picked up on clinical follow-up. CONCLUSIONS: The use of telemedicine has shown to be a feasible method in remote diagnosis of SSIs. Telemedicine is a useful adjunct for clinical practice in LMICs to decrease loss to postsurgical follow-up.
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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".