Assessment of Antioxidant Enzyme Superoxide Dismutase (SOD) in Oral Cancer: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective. The present article aims to comprehensively review the existing literature on superoxide dismutase (SOD) levels, an antioxidant enzyme, in oral cancer. Method. An extensive literature search was conducted across various databases, including PubMed, Wiley Online Library, Science Direct, and Cross Reference, spanning 1998–2023. At the outset, 1,177 articles were initially identified, and 907 studies were excluded due to irrelevance or duplication of the research question. Subsequently, 270 articles underwent screening evaluation, resulting in the selection of 85 articles meeting the inclusion criteria. Following this, 68 articles underwent a full-text comprehensive assessment, and ultimately, 39 were chosen for data extraction. The risk of bias in the designated articles was assessed using the Newcastle–Ottawa Scale. Finally, 13 studies were meticulously selected, offering consistent data for the ensuing meta-analysis. Meta-analysis was executed using comprehensive meta-analysis (CMA) version 3 software (Bio Stat Inc., Englewood, NJ, USA). The meta-analysis findings revealed a statistically significant decrease in SOD levels in both erythrocyte samples ( P<0.001 ) and tissue samples ( P<0.05 ) among individuals with oral cancer (OSCC) compared to the normal control group. Conversely, the analysis of three studies on salivary samples demonstrated a significant increase ( P<0.05 ) in SOD levels in the oral cancer group compared to the healthy controls. Conclusion. This systematic review underscores a statistically significant decline in SOD levels observed across diverse bio-samples in individuals with oral cancer, indicating an excess of oxidative stress (OS). Additional research is needed to delve into the relationship between SOD levels and clinic–pathological prognostic markers within the oral cancer cohort. Such investigations have the potential to significantly contribute to the development of prognostic tools grounded in OS, thereby guiding strategies for treatment planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".