Facial Reanimation Surgery: An Investigation on the Role of Online Information Sharing in Patient Education and Decision Making
Bibliographic record
Abstract
Purpose: The emergence of facial reanimation surgery as a reconstructive option has sparked a growing interest among patients with facial paralysis, leading to an increase in patients seeking and sharing information on these surgical modalities. This study evaluated the role of social media in information-sharing on facial reanimation surgery. Methods: We identified 630 Facebook groups based on the initial keyword search for “facial paralysis” and “Bell's palsy.” Groups with < 100 members, non-English content, or restricted access were excluded. Within each group, searches were conducted for terms related to surgery and posts were categorized as sharing information, seeking information, sharing support, seeking support, or sharing appreciation. Results: The search yielded 630 groups; 21 groups met the inclusion criteria (average size = 4037, largest = 31 400). Facial reanimation surgery was discussed in 15 groups, with 487 relevant posts tabulated. In the sharing information axis, posts were related to personal experiences (63%), alternatives (14%), link shares (7%), surgeon/center (5%), general recovery progression (8%), objective information on surgical modality (1%), objective information on nerve injury (1%), and general information on relevant medical research (1%). In the seeking information axis, posts were related to personal experience (71%), objective information (12%), surgeon/center (4%), second opinion (13%), and alternatives (1%). Conclusion: Social media is an essential source of information and support for people with facial paralysis. These study findings will inform the implementation of future knowledge translation efforts to maximize education and subsequent uptake of facial reanimation reconstructive surgery.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".