Use of Social Media and an Online Survey to Discuss Complex Reconstructive Surgery: A Case of Upper Lip Reconstruction with 402 Responses from International Microsurgeons
Bibliographic record
Abstract
Background The best reconstructive strategy for upper lip defects is still in debate. The purpose of this study was to analyze the decisions made by international microsurgeons, who were participated through online questionnaire, distributed by email and social media network. Materials and Methods A case of a two-thirds upper lip oncologic defect was presented via an online questionnaire and 402 microsurgeons replied their treatment options. The data were then analyzed according to the geographic area, microsurgical fellowship, seniority, and subspecialty. All the data were analyzed using SPSS 22. Results A total of 27.7% of microsurgeons chose a free flap, while 72.3% chose a local/pedicle flap as their preferred method for reconstruction. The most common choice of free and local/pedicle flaps was radial forearm (73.6%) and Abbé (36.2%), respectively. The microsurgeons in Europe preferred local/pedicle flaps than free flap when compared with Middle/South America, Asia-Pacific, Africa and South Asia/Middle East (11.6% versus 50%, 43.4%, 29.3% and 27.3%, respectively, multivariant p < 0.05). The microsurgeons with microsurgical fellowships preferred to use free flaps (32.9% versus 17.5%, multivariant p = 0.021). There was no difference for the seniority and specialty of the microsurgeons. Conclusions The online questionnaire is valuable and feasible for obtaining experts' opinions. This study provides a current global overview of surgical preferences for this common complicated clinical scenario.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".