Patient perspectives and self‐rated knowledge of nerve transfer surgery for restoring upper limb function in spinal cord injury
Bibliographic record
Abstract
BACKGROUND: Nerve transfer surgery has the potential to restore upper limb function in patients with spinal cord injury (SCI); however, there has been limited exploration of patient perception of nerve transfer. OBJECTIVE: To explore the perspectives of patients with chronic SCI (>2 years from injury) on nerve transfer surgery, and to determine if an educational intervention improved participants' perceived knowledge levels about the procedure. DESIGN: Mixed-methods study including qualitative semi-structured interviews and self-reported rating scales. Pre- and post-interviews were completed following an educational presentation. SETTING: Two local SCI clinics. PARTICIPANTS: Ten patients with chronic traumatic SCI and neurological level of injury C3-C7 (motor complete or incomplete), recruited via snowball sampling (six male, four female). INTERVENTION: An educational slide presentation on nerve transfer concepts. MAIN OUTCOME MEASURES: The primary study outcome measure is the participants' responses to interview questions. The secondary study outcome measure is their self-reported knowledge levels of nerve transfer before and after education. RESULTS: Regaining upper limb function was a priority for all participants. Although most participants had heard of nerve transfer, none were offered it at the time of their SCI, and only two stated that they had any peers who had undergone the procedure. The educational module significantly increased self-rated scores on understanding of nerve transfer (p < .05). Although all participants were open to nerve transfer after the educational module, they described weighing different factors, including (1) potential for loss versus gain of function, (2) inadequate knowledge about nerve transfer, (3) recovery time, and (4) determining their eligibility for the surgery. CONCLUSIONS: These findings suggest that people with SCI have limited understanding of nerve transfer as a potential option and would benefit from educational opportunities to help them make informed decisions. This study may inform the development of patient resources to improve pre-surgical consultation and informed decision-making.
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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".