Incidence Rates in Low-Grade Primary Brain Tumors: Are There Differences Between Men and Women? A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Incidence rates of adult low-grade primary brain tumors have previously been widely analyzed nationwide across the world, and most of these studies include data on incidence rates in men and women separately. However, to our knowledge, no worldwide international comparison has been made on possible differences in incidence rates of low-grade brain tumors between men and women. The primary aim was to review the incidence rates between men and women in adult low-grade primary brain tumors. METHODS: We searched for published articles in internationally peer reviewed journals that were identified through a systematic search of PubMed. Because of difficulties in interpreting data, we excluded all studies only including patient data before the second edition of World Health Organization (WHO) histological classification system of brain tumors (1993). We also made an overall analysis to calculate incidence rates of low-grade brain tumors in men and women separately. RESULTS: A total of 14 studies from the United States and Europe were reviewed. Overall mean age-adjusted incidence rate in men was 1.07 per 100,000 compared to 1.70 per 100,000 in women. No significant difference was seen in age-adjusted incidence rate between genders (Mann-Whitney U test; P = 0.8347). No significant trend of age-adjusted incidence rate was seen in male patients (P = 0.757) nor in women (P = 0.354). CONCLUSION: The results must be interpreted with caution and more large international studies are warranted and should be made in a standardized manner differing low-grade tumors from high-grade tumors according to the WHO 2007 brain tumor classification system. Also future studies should always state the ICD-O histology coding to ease future interpretations.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".