Prospects in the Application of Photodynamic Therapy in Oral Cancer and Premalignant Lesions
Bibliographic record
Abstract
Oral cancer is a global health burden with significantly poor survival, especially when the diagnosis is at its late stage. Despite advances in current treatment modalities, there has been minimal improvement in survival rates over the last five decades. The development of local recurrence, regional failure, and the formation of second primary tumors accounts for this poor outcome. For survivors, cosmetic and functional compromises resulting from treatment are often devastating. These statistics underscore the need for novel approaches in the management of this deadly disease. Photodynamic therapy (PDT) is a treatment modality that involves administration of a light-sensitive drug, known as a photosensitizer, followed by light irradiation of an appropriate wavelength that corresponds to an absorbance band of the sensitizer. In the presence of tissue oxygen, cytotoxic free radicals that are produced cause direct tumor cell death, damage to the microvasculature, and induction of inflammatory reactions at the target sites. PDT offers a prospective new approach in controlling this disease at its various stages either as a stand-alone therapy for early lesions or as an adjuvant therapy for advanced cases. In this review, we aim to explore the applications of PDT in oral cancer therapy and to present an overview of the recent advances in PDT that can potentially reposition its utility for oral cancer treatment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".