The First Smartphone Application for Microsurgery Monitoring
Bibliographic record
Abstract
Sir: It was with great interest that we read the article entitled “The First Smartphone Application for Microsurgery Monitoring: SilpaRamanitor” by Kiranantawat and colleagues.1 In this article, the authors demonstrate a novel use of smartphone technology to improve the quality of care in postoperative monitoring of free flap procedures. It brought to mind two comments. First, this application demonstrates high sensitivity and specificity and appears to rely on the ability of the app to identify color changes between normal and vascularly compromised skin. Do the authors feel that the 3.3-cm2 field comparison, referenced in the article, accurately captures overall flap viability? The authors state that expert analysis of time-series photography alone allowed for earlier detection of flap compromise.1 Will the authors rely on a combination of direct app detection and expert analysis when they adopt this technology for flap monitoring? Here at Women’s College Hospital, University of Toronto, Toronto, Ontario, Canada, we are performing smartphone follow-up among our ambulatory breast reconstruction patients using a standardized quality-of-recovery questionnaire and surgical-site photography.2 We too have found that the expert analysis of the series of photographs generated by the smartphone allows for easy comparison and earlier detection of complications. Similarly, the best performing dermatology app (98 percent sensitivity) for melanoma detection relies on direct analysis of the images by a board-certified dermatologist.3 Second, the authors’ recognition of the low cost of smartphone technology resonates, given the current emphasis on cost-effectiveness interventions in the literature.4 We encourage the authors to attach a cost-effectiveness study to their prospective study, as this information is essential, and proper cost-effectiveness studies are few and far between in the e-health/m-health literature.5 DISCLOSURE The authors have no financial interest to declare in relation to the content of this communication. The smartphone follow-up project mentioned in this article is funded by a Canadian Institutes of Health Research e-Health Catalyst grant. Dr. Semple is a shareholder in QoC Health, Inc., which holds the intellectual property rights for the smartphone follow-up technology mentioned in this article. Kathleen A. Armstrong, M.D. Division of Plastic and Reconstructive Surgery Department of Surgery Peter C. Coyte, Ph.D. Institute of Health Policy, Management and Evaluation John L. Semple, M.D., M.Sc. Division of Plastic and Reconstructive Surgery Department of Surgery University of Toronto Toronto, Ontario, Canada
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".