Cancer Recurrence in Operated Primary Oral Squamous Cell Carcinoma Patients Seems to Be Independent of the Currently Available Postoperative Therapeutic Approach: A Retrospective Clinical Study
Bibliographic record
Abstract
Despite advances in treatment, recurrence rates in oral squamous cell carcinoma (OSCC) remain high. Prognostic outcomes vary in terms of local recurrence, metastasis, and overall survival. A retrospective cohort analysis was conducted on OSCC patients who underwent primary surgery at the Department of Craniomaxillofacial and Facial Plastic Surgery, University Medical Center Frankfurt, between January 2014 and December 2020. Demographic data, tumor characteristics, surgical details, intraoperative frozen section results, and recurrence patterns were first assessed for availability. Subsequently, the available data relevant to each endpoint were analyzed. A total of 169 patients were analyzed (mean age: 64 years). The tongue was the most affected site and had the highest recurrence rate, followed by the floor of the mouth. Overall, 24.3% of patients experienced recurrence, with most cases occurring within the first year. T2 tumors had the highest recurrence rates. Between patients with and without adjuvant therapy, recurrence rates were comparable. Positive surgical margins were more common in recurrence cases, but no significant correlation was found between margin status and recurrence in patients without adjuvant therapy. Based on the analyzed data, achieving recurrence-free survival in OSCC does not solely depend on surgical technique or adjuvant therapy. Instead, early recognition of individual tumor characteristics and even tumor biology should guide personalized treatment planning. Notably, tumors of the tongue and floor of the mouth exhibited high recurrence rates regardless of disease stage, raising the question of whether primary chemoradiotherapy (CRT) could achieve better outcomes than surgery. Further studies are needed to evaluate the role of CRT as a first-line treatment for OSCC in these locations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".