Results of reoperation for failed ulnar nerve surgery at the elbow: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The clinical results of reoperation for recurrent or persistent ulnar nerve compression at the elbow have not been clearly determined. The aim of this review was to determine overall improvement, residual pain, and sensory and motor deficits following reoperation regardless of the type of primary surgery performed for this condition. METHODS: In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations, a systematic review and meta-analysis of studies was performed. An independent librarian performed a literature search using Ovid MEDLINE, Embase, CINAHL, and the Cochrane Central Register of Controlled Trials (CENTRAL). The Newcastle-Ottawa Scale and the quality appraisal tool described by Moga et al. were used to assess the quality of included case series. RESULTS: Of the 278 retrieved studies, 16 were eligible for analysis and included a total of 290 patients with failed surgery for ulnar nerve entrapment at the elbow. Relief of symptoms after reoperation was reported in 85% of patients. A decrease in pain was noted in 85% of the patients (95% CI 75%-93%). Only 2.4% of patients with preoperative pain experienced worse pain after reoperation. Motor and sensory function improvement was noted in 77% (95% CI 63%-88%) and 77% (95% CI 61%-90%) of cases, respectively. Complete recovery from signs and symptoms at the final follow-up was noted in 23% of elbows (95% CI 16%-31%). CONCLUSIONS: Although the level of evidence of the included studies was low, the majority of patients had relief from their complaints after reoperation for recurrent or persistent ulnar nerve compression at the elbow following a previous surgery. The success rate of surgical treatment for a failed surgery was quite remarkable since almost a quarter of the patients completely recovered. Therefore, the authors recommend reoperation as a serious option for patients with this condition.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".