Marginal versus Segmental Mandibulectomy in the Treatment of Oral Cavity Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Introduction Oral cavity squamous cell carcinoma (OCSCC) is the most common malignancy in the oral cavity. Two types of mandibular resections have been described: the segmental mandibulectomy and the marginal mandibulectomy. Both may have a different impact over the quality of life, oncological prognosis, and functional or aesthetic result. Objectives The aim of this study was to systematically explore the literature to determine the survival outcomes and disease control rates in patients who underwent segmental or marginal mandibulectomy for OCSCC with histological evidence of cortical and medullary bone invasion. Data Synthesis This review involved a systematic search of the electronic databases MEDLINE/PUBMED, Google Scholar, Ovid Medline, Embase, and Scopus including articles from 1985 to 2019. Fifteen articles were included for qualitative analysis and 11 articles were considered for meta-analysis calculations. All of them correspond to retrospective cohort studies. Conclusion This systematic review reveals the low-level evidence regarding the impact over local control or survival according to the type of mandibulectomy. Our results need to be considered with precaution according to the limited evidence available. We just found difference regarding the 5-year disease-free survival, and a tendency in favor of segmental mandibulectomy was confirmed when medullary invasion was evident.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".