Evaluation of Changes in Salivary Lactate Dehydrogenase Level for Detection of Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis Study
Bibliographic record
Abstract
Background: Research has examined the relationship between salivary lactate dehydrogenase (LDH) levels and head and neck squamous cell carcinoma (HNSCC) screening and prognosis. Due to biochemical changes in cancer cells and increased production of lactate products in the body. The present systematic review aims to evaluate the changes in salivary LDH levels in HNSCC patients. Methods: The present study is a systematic review and meta-analysis. The data were collected by searching PubMed, Science Direct, Scopus, Web of Science, and Google Scholar from 2000 to 2021. The heterogeneity of the articles was analyzed using I 2 and TAU 2 . Results: After searching the databases, of 988 articles, 665 duplicated articles were excluded by adopting the inclusion and exclusion criteria. So, 25 articles were primarily selected to be reviewed and evaluated for quality. Finally, 19 articles were selected and analyzed according to the Newcastle–Ottawa checklist. A total of 642 HNSCC patients were reviewed. The meta-analysis showed salivary LDH levels in the HNSCC group were higher than the control group (mean difference = 0.675, standard error = 0.058) ( P < 0.001). Conclusions: As the research results showed, a significant correlation was observed between salivary LDH levels and HNSCCs. So, LDH can be employed as a valuable and minimally invasive biomarker in head and neck cancer screening and prevention.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".