Photodynamic therapy: a review and its prospective role in the management of oral potentially malignant disorders
Bibliographic record
Abstract
With the unreliability of epithelial dysplasia as a predictor to determine the risk of future malignant development, subjectivity associated in evaluating dysplasia by pathologists and paucity of biomarkers that could accurately predict the progression risks in oral potentially malignant disorders (PMDs), eradication of the lesions appears to be the most desirable approach to minimize the risk of invasive cancer formation. Interventions, such as surgery and chemoprevention, have not shown promising long-term results in the treatment of these lesions, and lack of guidelines and general consensus on their management has incited much anxiety and doubts in both patients and community clinicians. Topical photodynamic therapy (PDT) is a minimally invasive and minimally toxic technique that in recent years has shown great promise in the management of PMDs. In this review, we describe the historical developments in the field of PDT, its basic mechanisms, as well as related clinical studies, and its challenges in the management of oral PMDs. Based on its high efficacy and low side effects, its high patient acceptance/compliance, the simplicity of the procedure and its minimal pretreatment preparation, topical PDT is believed to have potential to play an important role in the management of PMDs, especially of the low-grade dysplasia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".