Oxidative stress markers as a diagnostic tool in oral cancer and premalignant lesions: A systematic review
Bibliographic record
Abstract
Background. Oral cancer is the eighth leading cause of cancer-related mortality worldwide, particularly prevalent in regions with high tobacco and betel nut consumption. The associated high mortality and morbidity rates could be reduced through early detection, prompting researchers to focus on identifying early markers of carcinogenesis. Methods. This systematic review evaluated the effectiveness of oxidative stress markers as diagnostic tools. An electronic search was conducted in PubMed and Google Scholar to identify case‒control studies published between January 2000 and June 2024 that explored the use of oxidative stress markers as diagnostic biomarkers. A manual search was also performed in relevant journals, including oral oncology and oral diseases. Initially, 38 studies were screened, and after applying the inclusion criteria, only nine studies were included. The Newcastle-Ottawa Scale (NOS) tool was used to assess the risk of bias. Results. Eight studies were conducted in India, while one was from Saudi Arabia. These studies analyzed oxidative stress markers in oral squamous cell carcinoma (OSCC), oral submucous fibrosis (OSMF), and leukoplakia. Control groups were matched based on age and sex, with only two studies also considering socioeconomic status. A significant difference (P<0.05) in oxidative stress marker levels was observed between cases and controls, particularly in patients with OSCC and OSMF. Conclusion. Oxidative stress markers show promise as diagnostic and prognostic indicators. Standardized methodologies and therapeutic approaches targeting oxidative stress could enhance early detection and treatment, especially in resource-limited settings. However, the findings must be interpreted with caution due to methodological limitations, geographic bias, and the lack of inclusion of grey literature.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".