Epidemiology of Malignant and Non-Malignant Primary Brain Tumors in Jordan
Bibliographic record
Abstract
BACKGROUND: There is lack of knowledge on the epidemiological characteristics of brain tumors in Middle Eastern countries. The objective of this study was to study the epidemiological features of primary brain tumors in Jordan. METHODS: We conducted a prospective cohort study incorporating data from 16 hospitals in Jordan during a 1 year period (May 1, 2011-April 30, 2012). All primary brain tumors diagnosed in Jordan during the study period were identified. The following parameters were retrieved from patients' files: age, gender, histological type, and location. The demographic data of the country was obtained from the National Department of Statistics. RESULTS: A total of 313 primary brain tumors were identified during the study period. The incidence of primary brain tumors in Jordan among the general population was 5.01 per 100,000 person-years (5.38 in females and 4.65 in males). The incidence in pediatric, adult, and elderly patients was 2.09, 7.29, and 14.38 per 100,000 person-years, respectively. The most common histological types were meningioma (26.2%), glioblastoma (18.9%), astrocytoma (14.1%), and pituitary adenoma (9.3%). CONCLUSIONS: The incidence of primary brain tumors in the Jordanian population is relatively low, in part due to the young age of the general population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".