Seasonal patterns of presentation in primary malignant brain tumors and metastases based on a retrospective neuropathologic database
Bibliographic record
Abstract
Abstract Seasonal variation in the occurrence of several classes of cancer has been observed in the past. However, evidence for such trends in adult central nervous system tumors is scant. We have analyzed the monthly occurrence rates of glioblastomas as well as carcinomas metastatic to the brain in 6,154 neurosurgical patients in Toronto selected from the University Health Network neuropathologic database over a seven-year period (July 2001 to June 2008). The electronic repository was representative of the patient population in southern Ontario, and the case accession dates in the database reflected the onset patterns of the selected tumor groups. A modification to Nam's alternative method to the Roger test was developed to statistically quantify the differences. The results demonstrated significant cyclical occurrence rates of glioblastomas with seasonal peaks in March, June, September and December. Moreover, significant increases in the rates of carcinomas metastatic to the brain were found for January, April and August. Surprisingly, the monthly frequency for the two tumor groups resembled each other in peak/trough topology. Semiquantitative comparison of major histologic features between glioblastomas from a peak (March) and trough (November) month in the seven-year period was performed, revealing differences in the amount of perivascular lymphocytic inflammation. This novel observation may have profound implications for the understanding of the biology of adult central nervous system tumors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".