Telemedicine for triage: A systematic review of virtual consultation in hand trauma
Bibliographic record
Abstract
IntroductionTelemedicine involves the use of electronic communication systems to exchange medical information between health professionals or with patients. With the increasing demand for telemedicine delivery, catalyzed by the COVID-19 pandemic, it is important to investigate the use of telemedicine within the field of hand surgery. The aim of this study is to present the current state of telemedicine use in hand trauma, with a particular focus on accuracy of diagnosis, cost effectiveness, and access to care.MethodsAn online systematic review of MEDLINE, EMBASE, Pubmed and The Cochrane Library from inception to 16 May 2025 was completed. Data extracted included telemedicine medium used, accuracy of diagnosis, cost, impact on patient transfer volume, and timeline for assessment. Study quality was assessed using the MINORS scale.ResultsOf the 15 included studies, eight assessed diagnostic accuracy, four evaluated cost savings, four examined patient transfers, five reported on efficiency, and three investigated access to care. All studies assessing accuracy found telemedicine to be an accurate method of triaging and diagnosing patients. All studies assessing cost-effectiveness found telemedicine to be an effective cost-savings instrument. Telemedicine was also demonstrated to improve healthcare efficiency by decreasing the number of unnecessary patient transfers, reducing extra visits and unnecessary consultations and improve access to specialist care for patients in rural communities.ConclusionsThe current literature suggests that the application of telemedicine in initial hand trauma consultation was found appears satisfactory diagnostic accuracy, cost savings, reduced patient transfers, increased efficiency, and improved access to care when compared to traditional face-to-face triaging and diagnosis of hand traumas although evidence is largely observational.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".