Recurrent Oral Cancer: Current and Emerging Therapeutic Approaches
Bibliographic record
Abstract
Oral cavity cancer (OCC) is associated with high incidence of loco-regional recurrences, which account for the majority of treatment failures post-surgery and radiotherapy. The time-course of relapse manifestation and metastasis are unpredictable. Relapsed OCC represents a major clinical challenge in part due to their aggressive and invasive behaviors. Chemotherapy remains the only option for advanced OCC whenever salvage surgery or re-irradiation is not feasible, but its efficacy is limited as a result of the drug resistance development. Alternatives to use of different permutations of standard cytotoxic drugs or combinations with modulators of drug resistance have led to incremental therapeutic benefits. The introduction of targeted agents and biologics against selective targets that drive cancer progression has opened-up optimism to achieve superior therapeutic activity and overcome drug resistance because, unlike the non-selective cytotoxic, the target can be monitored at molecular levels to identify patients who can benefit from the drug. This review discusses the multifactorial aspects of clinical drug resistance and emerging therapeutic approaches in recurrent OCC, emphasizing recent advances in targeted therapies, immunotherapy, and potential relevance of new concepts such as epithelial-mesenchymal transition and cancer stem cell hypothesis to drug resistance.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".