BIOS-06. INCIDENCE RATE OF BRAIN METASTASES AMONG WOMEN WITH GYNECOLOGICAL CANCERS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract INTRODUCTION Brain metastases (BrM) among women with gynecological cancers (GCs) are considered rare events. With the advent new targeted therapies, women with GCs are living longer and the incidence of BrM remains to be fully explored. METHODOLOGY A comprehensive search for studies regarding GCs and BrM using controlled terms and keywords was conducted using Medline, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews on the Ovid platform, as well Google Scholar. Initial searches were performed in September 2023 and updated in December 2024. 2116 studies were screened and 97 studies were selected for full text review. Meta-regression analysis was performed to estimate the incidence rate ratio (IRR) of BrM in patients with ovarian cancer compared to those with other primary turmors. A random-effects meta-analysis using the REML estimator was performed to account for heterogeneity across studies, which was quantified using Cochrane’s Chi-squared test (Cochran’s Q). The analyses were performed using the R software (version 4.4.1). RESULTS A random-effects meta-analysis was performed on 35 studies that involved 81,585 patients with GC and available data regarding BrM incidence. The pooled incidence rate of BrM was 0.06 (95% CI: 0.05 - 0.08) per 100 person-months. There was substantial heterogeneity across the studies (τ² = 0.57), with an I² value of 93.4% (95% C.I: 89.94- 96.09). The Cochran’s Q test was statistically significant (Q=475, df=37, p <0.001) and no significant publication bias was observed (Egger’s test p = 0.622). Meta-regression analysis revealed that the incidence rate of BrM is 36% (IRR=1.36, 95% C.I: 0.81, 2.28, p=0.244) higher in patients with ovarian cancer compared to those with other GCs. CONCLUSION BrM among patients with GCs is a rare event in published literature, but further research in modern cohorts is needed.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.079 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".