Photodynamic therapy for early and recurrent oral cancers and pre-malignant lesions. Need of the hour for India
Bibliographic record
Abstract
• Prevalence of oral cancer: India has the highest incidence of oral cancers globally, majorly due to widespread tobacco consumption, with a significant number of users engaging in smokeless tobacco. • Photodynamic therapy (PDT): PDT as a less invasive, targeted, and repeatable treatment option that can significantly improve the quality of life for patients with oral cancers and pre-malignant lesions. • Case Studies: we present two case studies, of early and of recurrent oral cancer where PDT was utilized successfully:. - A 55-year-old patient with recurrent oral cancer treated with PDT in 2007, who remained disease-free for 13 years. - A 43-year-old patient with extensive pre-malignant lesions and early cancer treated in 2018, avoiding near-total glossectomy and preserving quality of life. Is on regular follow up. • Treatment details: both cases detail the PDT procedure, including the administration of the photosensitizing agent Temoporfin and the subsequent application of a diode laser, resulting in positive long-term outcomes. • Impact on quality and quantity of Life: PDT's ability to spare patients from debilitating surgeries has a profound impact on their quality of life, allowing them to continue their usual activities with minimal discomfort or disability with increase in survival. • Tobacco Cessation: the article emphasizes the critical need for effective tobacco cessation programs as part of the treatment protocol for oral cancer patients in India. • Management of Submucous Fibrosis: the use of the Triscare mouth opener device for managing submucous fibrosis, a condition often resulting from betel nut consumption in chewing tobacco, is discussed. • Implications for Indian Healthcare: the paper calls for the recognition of PDT as a need of the hour in the Indian healthcare setting, which could lead to improved clinical outcomes for a vast patient population suffering from oral cancers and pre-malignant conditions. Oral cancer rates in India surpass those in any other nation, with patients manifesting varied disease stages, including pre-malignant phases. Tobacco chewing promotes field cancerization, and related surgical interventions can be remarkably morbid. Advanced-stage patients with frequent recurrences undergo treatments that significantly degrade their quality of life. In such instances, Photodynamic Therapy (PDT) emerges as an invaluable tool, enhancing both the longevity and quality of life. This paper elucidates two such cases benefiting from PDT, spotlighting its potential in transforming cancer care in India.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".