Impact of surgical margins on recurrence and survival rate in patients with oral squamous cell carcinoma: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Oral squamous cell carcinoma (OSCC) accounts for approximately 90% of malignant neoplasms of the oral cavity. At early stages, the treatment of choice is surgical resection with clear margins, commonly defined as ≥5mm of tumor-free tissue. However, the optimal surgical margin in relation to recurrence and survival remains controversial. The objective of this study was to evaluate the impact of surgical margin status on local recurrence and overall survival in patients with OSCC through a meta-analysis. MATERIAL AND METHODS: An electronic search was conducted in Medline-PubMed, Web of Science, and Scopus up to January 2025. Two investigators independently selected the studies according to the inclusion criteria. The study included prospective and retrospective studies assessing patients with oral squamous cell carcinoma who underwent surgical treatment and reported data regarding surgical margin status, recurrence rates, and survival outcomes. The Newcastle-Ottawa Scale was used for non-randomized observational studies. Odds ratios were estimated with 95% confidence intervals, and forest plots were generated using random-effects or fixed-effects meta-analyses depending on heterogeneity. Sensitivity analyses and publication bias analyses were performed using funnel plots and Egger's test. All statistical analyses were conducted using Comprehensive Meta-Analysis software, version 3.0. RESULTS: Positive margins (<5mm) were significantly associated with a higher rate of local recurrence (OR=2.72; 95% CI: 2.04-3.62; p<0.001), while negative margins (≥5mm) were linked to a 1.58 -fold increase in the probability of 5-year survival (RR=0.63; 95% CI: 0.55-0.74; p<0.001). CONCLUSIONS: Surgical margin status is a prognostic factor for locoregional control and overall survival in OSCC. A cutoff value of ≥5mm is proposed as the optimal surgical margin.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.042 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".