Neoadjuvant Treatment Approaches to Oral Cancer
Bibliographic record
Abstract
Background/Objectives: The high prevalence of oral squamous cell carcinoma (OSCC) has driven the development of surgical and oncologic techniques to improve survival. Despite advancements in surgical technique and chemoradiation protocols, survival rates for locally advanced OSCC remain low due to high recurrence and metastasis. This has driven the exploration of neoadjuvant treatment protocols as a potential pathway towards improving organ-preserving resection, de-escalating adjuvant treatment, and improving overall and recurrence-free survival. Methods: This is a narrative review summarizing the current literature and ongoing trials on neoadjuvant treatment for OSCC. PubMed was searched using a snowballing technique to capture all relevant clinical trials. Results: 21 clinical trials were identified. Although neoadjuvant chemotherapy was associated with favorable pathologic outcomes, clinical trials demonstrated variable survival outcomes. In contrast, neoadjuvant immunotherapy for OSCC demonstrated improved pathologic responses and survival outcomes, with a low incidence of grade 3–4 adverse events. Conclusions: Neoadjuvant therapy in OSCC shows promise but does not yet constitute standard of care. Neoadjuvant immunotherapy has encouraging response rates and lower treatment-related toxicities in comparison to neoadjuvant chemotherapy. Although recent clinical trials have presented strong evidence to support the use of neoadjuvant immunotherapy in the treatment of locally advanced OSCC, further randomized trials are required to establish standardized neoadjuvant protocols and biomarkers to assess treatment response.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".