Exploring Surgical Oncology-Based Comparative and Systematic Analysis of the Diagnostic Potential of Various Novel Biomarkers in Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Head and neck squamous cell carcinoma (HNSCC) is a widespread malignancy and has high morbidity and mortality rates. Identifying reliable biomarkers can help in improvement of diagnosis, prognosis, and treatment response. This systematic review aimed to evaluate the diagnostic potential of various novel biomarkers against HNSCC. A comprehensive search was taken place using PubMed, Scopus, Web of Science, and Google scholar that covered range of studies published from 2019 to 2025. The search focused on biomarkers that related to HNSCC and its correlation with clinical outcomes such as survival rates and treatment response. Studies were selected on the basis of predefined eligibility criteria and included observational and experimental study designs. Data extraction was performed by two independent reviewers. Study quality was assessed using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool for respective study designs. The quality of the evidence was evaluated using the GRADE approach. A total of 12 studies were included in this review after full screening process. The studies evaluated several biomarkers that indicated towards significant correlations between certain biomarkers and HNSCC prognosis. Elevated levels of expression of specific biomarkers were associated with poor survival outcomes, while others showed promise in the prediction of recurrence and treatment efficacy. The review highlighted the effectiveness of novel biomarkers for improving the HNSCC management. However, further validation of these findings and establish biomarkers for clinical use.
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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".