Neutrophil to lymphocyte ratio as an assessment tool to differentiate between uterine sarcoma and myoma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: This systematic review and meta-analysis aimed to determine the potential value of neutrophil to lymphocyte ratio (NLR) as an assessment tool in the clinical distinction between uterine sarcoma and uterine leiomyoma. METHODS: We comprehensively searched Web of Science, Scopus, and PubMed for relevant papers published before March 19, 2023. The standardized mean difference (SMD) was provided, along with a 95% confidence interval (CI). The random-effects model was employed to derive pooled effects due to the high levels of heterogeneity. The Newcastle-Ottawa scale was used for the quality assessment. Our study was registered in PROSPERO (CRD42023478331). RESULTS: Overall, seven articles were included in the analysis. A random-effect model revealed that patients with uterine sarcoma had higher NLR levels compared to those with uterine myoma (SMD = 0.60, 95% CI = 0.22-0.98; p = 0.002). In the subgroup analysis according to sample size, we found that patients with uterine sarcoma had elevated levels of NLR compared to those with uterine myoma in either large studies (SMD = 0.58, 95% CI = 0.04-1.13; P < 0.001) or small studies (SMD = 0.64, 95% CI = 0.33-0.96; P = 0.32). In the sensitivity analysis, we found that the final result was not significantly changed when single studies were removed, suggesting that the finding of this meta-analysis was stable. The pooled sensitivity of NLR was 0.68 (95% CI = 0.61-0.73), and the pooled specificity was 0.64 (95% CI = 0.59-0.69). CONCLUSION: NLR might be utilized as an assessment tool in clinics to help clinicians differentiate between patients with uterine sarcoma and those with myoma.
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 | 0.022 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.040 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".