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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".