Open versus endoscopic in situ decompression in cubital tunnel syndrome: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: We conducted this systematic review and meta-analysis to compare the clinical efficacy and safety between open and endoscopic in situ decompression surgery methods for cubital tunnel syndrome (CuTS). METHODS: PubMed, Medline, Embase, Cochrane Library and CNKI were searched for eligible studies. The data were extracted by two of the coauthors (WL, BYF) independently and were analyzed using RevMan statistical software, version 5.1. Relative risks (RRs) and 95% confidence intervals (CIs) were calculated. Cochrane Collaboration's Risk of Bias Tool and the Newcastle-Ottawa Scale were used to assess the risk of bias. RESULTS: Seven studies were included for systematic review, and six studies were included for meta-analysis. The CuTS patients received open in situ decompression (OISD) or endoscopic in situ decompression (EISD). A pooled analysis of postoperative Bishop score showed that the difference was not statistically significant between the EISD group and the OISD group (RR = 0.99, 95% CI = 0.88-1.12, P = 0.88). The overall estimate of postoperative satisfaction between the EISD group and the OISD group was not found to be significant (RR = 0.98, 95% CI = 0.89-1.08, P = 0.70). The overall estimate of complications (RR = 0.88, 95% CI = 0.24-3.29, P = 0.85) suggested that the difference was not statistically significant. CONCLUSIONS: EISD and OISD for treating CuTS have equivalent efficacy for postoperative clinical improvement, whereas the incidences of complications of endoscopic surgical procedure were also same as those with the open surgical procedure. In situ decompression (especially EISD, with minor intraoperative trauma) could be treated as a valuable alternative to treat CuTS.
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.037 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".