Nerve transfer surgery in spinal cord injury: online information sharing
Bibliographic record
Abstract
BACKGROUND: Nerve transfer to improve upper extremity function in persons with cervical spinal cord injury (SCI) is a new reconstructive option, and has led to more people seeking and sharing surgical information and experiences. This study evaluated the role of social media in information-sharing on nerve transfer surgery within the SCI community. METHODS: Data were collected from Facebook, which is the favored information-sharing platform among individuals seeking medical information. Searched terms included 'spinal cord injury' and 'SCI' and excluded groups with: less than two members (n = 7); closed groups (n = 2); not pertaining to SCI (n = 13); restricted access (n = 36); and non-English (n = 2). Within public and private accessed groups, searches were conducted for 'nerve', 'transfer', 'nerve transfer', and 'nerve surgery'. Each post about nerve transfer, responses to posts, and comments about nerve transfer in response to unrelated posts were tabulated. Thematic content analyses were performed and data were categorized as seeking information, sharing information, sharing support, and sharing appreciation. RESULTS: The search yielded 99 groups; 35 met the inclusion criteria (average size = 2007, largest = 12,277). Nerve transfer was discussed in nine groups, with 577 total mentions. In the seeking information axis, posts were related to personal experience (54%), objective information (31%), surgeon/center performing the procedure (9%), and second opinion (4%). At least 13% of posts were from individuals learning about nerve transfers for the first time. In the sharing information axis, the posts: shared personal experience (52%); shared objective information (13%); described alternative treatment (3%); tagged someone to share information (11%); linked to outside resources (12%); and recommended a specific surgeon/center (9%). CONCLUSION: Social media is an important source of information and support for people with SCI. There is a paucity of information on nerve transfers. These study findings will inform implementation of future education strategies.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".