Descriptive statistics of dataset from the meta-analysis and meta-regression analysis on prognostic significance of pre-treatment systemic hemato-immunological indices of cervical cancer patients
Bibliographic record
Abstract
In this study, we perform a meta-analysis and meta-regression analysis for the article entitled “Prognostic value of systemic hemato-immunological indices in uterine cervical cancer: A systemic review, meta-analysis, and meta-regression of observational studies.” [1] We implemented quantitative meta-analyses and time series meta-regression analysis to determine whether systemic hemato-immunological indices, such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), thrombocyte-to-lymphocyte ratio (TLR), and C-reactive protein/albumin ratio (CAR) are associated with an increased risk of cervical collision cancer. In all, 9558 patients from 22 studies were included after a systematic data search, performed comprehensively using the following databases: MEDLINE, Web of Science, Embase, and Cochrane. The meta-analysis was conducted with a random-effects model using the Review Manager software (Revman version 5.3). The overall survival (OS), disease-free survival (DFS), and progression-free survival (PFS) data were compared among each observational study. All data are expressed as hazard ratios (HRs) and 95% confidence intervals (CIs), and were calculated using the generic inverse of variance method. Statistical heterogeneity was quantified using Cochrane's Q statistic and Higgins I2 statistic. Subgroup analysis was performed to investigate the sources of heterogeneity. Furthermore, quality assessment of the included datasets was presented according to the Newcastle-Ottawa Scale method. Additionally, sensitivity analysis was conducted to explore the sources of heterogeneity and analyze whether the results were stable and reliable. Meta-analysis random-effect approach was used for the regression to evaluate the effect of age, presence of squamous cell carcinoma patients, and number of evaluated NLR and PLR parameters on patient survival.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".