Efficacy of light based detection systems for early detection of oral cancer and oral potentially malignant disorders: Systematic review
Bibliographic record
Abstract
BACKGROUND: Earlier detection of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMD) is essential for dental professionals to improve patient survival rates. The aim of this systematic review is to to evaluate the effectiveness of devices that utilise the principles of chemiluminescence and tissue autofluorescence as adjuncts in the detection of OSCC and OPMD. MATERIAL AND METHODS: The electronic retrieval systems and databases searched for relevant articles were PubMed [MEDLINE] and Science direct. The search was for limited articles published in English or with an English abstract and articles published during the period from January 2005 to April 2014. Clinical trials utilized ViziLite, Microlux TM/DL and Visual Enhanced Light scope (VELscope) for early detection of OPMD and OSCC. RESULTS: Twenty primary studies published satisfied our criteria for selection - 10 utilised chemiluminescence and 10 tissue autofluorescence. Senstivity of Vizilite for detecting OSCC nad OPMD ranged from 77.1 % to 100% and specificity was low that ranged from 0% to 27.8%.Most have shown that chemiluminescence increases the brightness and margins of oral mucosal white lesions and thus assist in identification of mucosal lesions not considered under Conventional visual examination. However, it preferentially detects leukoplakia and may fail to spot red patches. Clinical trials demonstrated that sensitivity of VELscope in detecting malignancy and OPMD ranged from 22 % to 100 % and specificity ranged from 16 % to 100%. Most studies concluded that VELscope can help the experienced clinician to find oral precursor malignant lesions. But it couldnot differentiate between dysplasia and benign inflammatory conditions. CONCLUSIONS: Both devices are simple, non-invasive test of the oral mucosa but are suited for clinicians with sufficient experience and training. More clinical trials in future should be conducted to establish optical imaging as an efficacious adjunct tool in early diagnosis of OSCC and OPMD.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".