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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it