Prognostic Value of Primary Total Glossectomy in Tongue Cancer: A Systematic Review and Meta-Analysis of Survival Outcomes
Bibliographic record
Abstract
Background/Objectives: Total glossectomy (TG) is among the most radical operations in head and neck oncology. While it can achieve local control in advanced oral tongue squamous cell carcinoma, survival and functional outcomes are inconsistently reported, and pooled estimates remain limited. This study aimed to systematically evaluate survival, functional recovery, and prognostic factors following primary TG. Methods: We conducted a proportional meta-analysis of studies reporting outcomes after primary TG for oral tongue squamous cell carcinoma. Studies combining TG with laryngectomy, salvage settings, or second primary tumors were excluded. Two reviewers independently screened, extracted data, and assessed quality with the Newcastle–Ottawa Scale. Pooled 1-, 3-, and 5-year overall survival (OS) with 95% confidence intervals (CIs) was calculated using a random-effects model. Heterogeneity was quantified (Q, τ2, I2), and robustness was assessed with sensitivity analyses. Disease-free survival (DFS) and functional outcomes (swallowing, airway, speech) were narratively summarized due to inconsistent reporting. Results: Ten studies (1992–2022) comprising 261 patients met the criteria. Pooled OS was 81% (95% CI, 71–90) at 1 year, 55% (95% CI, 41–68) at 3 years, and 47% (95% CI, 27–67) at 5 years, with rising heterogeneity (I2 up to 89%). The post-2000 series showed improved 5-year OS (63%). Adverse prognostic factors included advanced T stage, nodal disease (N+), and positive margins. Functional recovery varied: 15–30% remained gastrostomy-dependent and 20–25% aspirated, while reconstruction and structured rehabilitation improved outcomes. Conclusions: Survival after TG declines beyond the first year, with under half surviving at 5 years, though modern outcomes appear better. Significant functional morbidity underscores the need for multidisciplinary care. Future biomarker-driven studies should refine patient selection and prognostic assessment.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.044 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".