Pain and Function Following Revision Cubital Tunnel Surgery
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to determine pain and functional outcomes following revision cubital tunnel surgery and to identify predictors of poor postoperative outcome. METHODS: A retrospective cohort study was conducted of all patients undergoing revision cubital tunnel surgery over a 5-year period at a high-volume peripheral nerve center. Intraoperative findings, demographic and injury factors, and outcomes were reviewed. Average pain, worst pain, and impact of pain on self-perceived quality of life were each measured using a 10-cm visual analog scale (VAS). Function was evaluated using pinch and grip strength, as well as the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire. Differences in preoperative and postoperative pain, strength, and DASH were analyzed using nonparametric tests. Predictors of postoperative average pain were evaluated using odds ratios and linear regression analyses. RESULTS: The final cohort consisted of 50 patients (mean age: 46.3 ± 12.5 years; 29 [68%] male) undergoing 52 revision ulnar nerve transpositions (UNTs). Pain VAS scores decreased significantly following revision UNT. Strength and DASH scores demonstrated nonsignificant improvements postoperatively. Worse preoperative pain and greater than 1 prior cubital tunnel procedure were significant predictors of worse postoperative average pain VAS scores. CONCLUSIONS: Patients can and do improve following revision cubital tunnel surgery, particularly as it relates to pain. Intraoperative findings during the revision procedure suggest that adherence to specific principles in the primary operation is key to prevention of secondary cubital tunnel syndrome.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".