Utility of a Novel Mobile Application (FLAPP) for Teaching Post-operative Monitoring of Microsurgical Anastomoses
Bibliographic record
Abstract
PURPOSE: Microsurgical reconstruction is an indispensable tool in Plastic Surgery. Early detection of microanastomosis failure is critical, but there is a paucity of teaching resources in postoperative monitoring. We recently developed a mobile application (Flap Assessment App; FLAPP), including microsurgery, clinical/Doppler assessments, and flap troubleshooting tutorials, and practice cases. This study tested the usefulness of this app. METHOD: Members of the University of Manitoba Department of Plastic Surgery used the FLAPP teaching app then completed a questionnaire assessing the app sections using a Likert scale. Qualitative analysis was performed. Preliminary results are presented. RESULTS: Participants included residents (43%), nurses (14%), attendings (4%) and physician assistants (PAs) (4%). Of residents, 50% were junior (PGY-1/2) and 50% senior (PGY-3/4/5). 50% of nurses/PAs had >5 years’ experience monitoring free flaps, while 33% had <1 year. 100% of participants agreed/strongly agreed that each tutorial section was useful. 100% of participants agreed/strongly agreed that case video quality and variety was acceptable; 86% agreed/strongly agreed that audio quality was acceptable. 100% of participants agreed/strongly agreed the app was useful for teaching and improving confidence/ability in monitoring microanastomoses; 100% agreed/strongly agreed the app should be incorporated into teaching curriculum and would recommend to other trainees. 93% of residents, nurses, and PAs agreed/strongly agreed the app would be beneficial to use prior to clinical assessments and would use the app to practice independently. CONCLUSION: The FLAPP teaching app contains tutorials and practice microsurgical cases useful for learning and improving confidence in post-operative microsurgical monitoring, beneficial for training prior to clinical assessments. Next steps include app updates based on feedback, and additional testing prior to wide release as a free teaching tool.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".