Hematological parameters as predictors of oral cancer prognosis: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis aimed to assess the prognostic value of pre-treatment hematological parameters, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and mean platelet volume (MPV), in patients with oral squamous cell carcinoma (OSCC). METHODS: A systematic search of PubMed, Embase, Scopus, Web of Science, ScienceDirect, and Google Scholar was conducted until April 2025. We included English-language observational studies reporting associations between NLR, PLR, MPV, and survival or clinicopathological outcomes in OSCC. Data extraction and risk of bias assessment using the Newcastle-Ottawa Scale were performed independently by two reviewers. Hazard ratios (HRs) and odds ratios (ORs) were pooled using a random-effects model. The certainty of evidence was evaluated using the GRADE. RESULTS: Thirty-five studies (approximately 7940 patients) were included. A high NLR was associated with worse overall survival (pooled HR 1.59, 95% CI 1.32-1.92) and disease-free survival (HR 1.66, 95% CI 1.31-2.10). PLR showed similar associations with overall survival (HR 1.58, 95% CI 1.29-1.94) and disease-free survival (HR 1.50, 95% CI 1.18-1.90). Between-study heterogeneity was moderate to high in this study. MPV findings were inconsistent and not pooled. CONCLUSIONS: Elevated NLR and PLR correlate with poorer outcomes in OSCC, with effect sizes varying by study design and cut-off selection. These blood-based indices may aid in risk stratification; however, prospective validation with standardized thresholds is required.
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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".