Antegrade versus retrograde facial nerve dissection in benign parotid surgery: Is there a difference in postoperative outcomes? A meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The primary aim of this meta-analysis was to test the null hypothesis of no difference in facial nerve dysfunction in studies that compared classical antegrade facial nerve dissection (AFND) versus retrograde facial nerve dissection (RFND) during benign parotid surgery. METHODS: A comprehensive search of PubMed, the Cochrane Central Register of Controlled Trials, Scopus, Google Scholar, Science Direct and relevant journals was undertaken up to June 27, 2018. Randomized controlled clinical trials (RCTs), controlled clinical trials (CCTs), and retrospective studies aimed at comparing the effect of AFND vs. RFND during parotidectomy were included. The outcome measures included facial nerve dysfunction, Frey's syndrome, recurrence, silaocele, salivary fistula, operating time length of hospital stay, and estimated blood loss. Pooled risk ratio (RR) and weighted mean differences (MD) with 95% confidence intervals were calculated using either a fixed-effects or random-effects model. RESULTS: Ten studies; four RCTs and five retrospective studies were included. There were 570 patients (319 in RFND group and 251 in AFND group). 481 patients in 9 studies reported the incidence rate of facial nerve dysfunction. No statistical significant difference was observed between both groups concerning the occurrence of transient or permanent facial nerve paralysis (p = 0.44 and 0.11 respectively). One out 10 studies reported the incidence rate of sialocele, however no statistical difference was observed between the two techniques. There was reduction in the operative time (19.30 min), amount of blood loss (25.08 ml) and amount of healthy salivary tissues removed (12.20 mm) in RFND compared with AFND. CONCLUSIONS: According to the results of the current review there is no evidence demonstrating a significant advantage of one approach over another, therefore, well-designed standardized RCTs are required.
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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 | 0.023 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.072 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".