Forgoing Upper-Extremity Reconstructive Surgery After Cervical Spinal Cord Injury: A Qualitative Prospective Cohort Study
Bibliographic record
Abstract
PURPOSE: Individuals with cervical spinal cord injury (SCI) rate restoration of upper extremity (UE) function as their top priority. Despite positive outcomes, rates of tendon transfer (TT) surgery remain low, particularly in the United States. The purpose of this study was to better understand why eligible individuals with SCI forgo surgery in an era of expanded treatment options using nerve transfers (NT). METHODS: Data were collected from adults with midcervical SCI who chose not to undergo UE surgery. A semistructured interview guide was developed and used to elicit information about the surgical decision-making used by the patients and their caregivers. Interviews were transcribed, coded by a team of researchers, and analyzed using conventional content analysis. Findings were summarized into themes. The study adhered to the Consolidated Criteria for Reporting Qualitative Research guidelines. RESULTS: This study included data on 16 primary study participants with SCI (94% men, mean age 39 years, median time since injury 9 years) and eight caregivers. Reasons to forgo both TT and NT surgery included the following: (1) fear of degradation of UE function, (2) desire for more gains in function than can be achieved by surgery, (3) adjustment to existing function, (4) competing priorities, (5) expect natural recovery or a cure, and (6) previous negative health care interactions. CONCLUSIONS: Despite the advent of newer NT that augment traditional TT options to restore UE movement in SCI, barriers to UE reconstruction persist. Providing detailed information on surgical risks, outcomes, and comparative data about gains from natural recovery alone could improve shared decision-making about these effective, but underutilized surgeries. CLINICAL RELEVANCE: This analysis examines reasons why people forgo upper-extremity surgery for cervical SCI despite the advent of newer NT that have expanded treatment options and decreased the need for postoperative immobilization required of traditional TTs.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".