Prognostic value of lymphocyte-to-monocyte ratio in patients with endometrial cancer: an updated systematic review and meta-analysis
Bibliographic record
Abstract
Background: Evaluating the risk of metastasis at diagnosis and the likelihood of future recurrence is crucial for the effective management of endometrial cancer (EC). While conventional prognostic indicators hold importance, they often fall short in predicting recurrence, especially in low-risk patients. This study evaluates the prognostic value of the lymphocyte-to-monocyte ratio (LMR) for overall survival (OS), disease-free survival (DFS), and cancer-specific survival (CSS) in EC patients. Methods: Eligible studies that provided pretreatment cutoff values of LMR, hazard ratios (HRs), and 95% confidence intervals (CIs) for OS, DFS, CSS, and progression-free survival (PFS) were included in this meta-analysis. Two independent reviewers collected and evaluated the data, and the quality of the included studies was assessed using the Newcastle Ottawa Quality Assessment Scale (NOS). Statistical analyses were performed using STATA software, and subgroup analyses were conducted by race, sample size, and age to assess the consistency of LMR's prognostic value across different population groups. Results: = 0.01). Subgroup analysis indicated that the prognostic value of LMR for OS was consistent across different races, age groups, and sample sizes. However, the correlation between LMR and DFS was influenced by median age, with younger patients (<60 years) showing a stronger association. Sensitivity analyses confirmed the robustness of these results, and Egger's test showed no significant publication bias. Discussion: LMR serves as a valuable prognostic marker for OS, DFS, and CSS in EC patients. Its predictive power remains significant across diverse population groups, underscoring its potential utility in clinical practice. Biological mechanisms linking inflammation and cancer support the role of LMR in prognosis, given the functions of lymphocytes and monocytes in tumor progression and immune response. These findings suggest that incorporating LMR into current prognostic models could enhance risk stratification for EC patients, particularly for identifying those at higher risk of recurrence despite being classified as low risk by traditional systems. In conclusion, LMR is a robust, independent prognostic factor for EC, with significant implications for improving patient management and outcomes through better risk stratification.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".