Latency period for radiological appearance of new intracranial metastases
Bibliographic record
Abstract
Introduction: This study sought to determine the overall and disease-specific latency period for radiological appearance of new intracranial metastases for patients with metastatic involvement of the brain. Methods: A retrospective chart review of patients with intracranial metastases between 2008–2010 was conducted. For each patient, the following were recorded: cancer type, gender, age at diagnosis of primary cancer and first intracranial metastases, treatments (chemotherapy, whole-brain-radiotherapy (WBRT), radiosurgery), and latency period for radiological appearance of new intracranial metastases. Results: 137 patients with multiple metastatic tumors were included in our study. Majority (>90%) of patients received chemotherapy and WBRT. The latency periods for appearance of new metastases for different cancer types were (in months): breast 12.7, lung 11.3, colorectal 9.0, melanoma 6.6, renal cell 8.1, other 8.1. The overall average latency period was 10.1 months. There was no relation between latency period for new metastases and the following: age at diagnosis of metastases(p=0.174), age at treatment(p=0.199), and cancer type(p=0.124). The latency period for new metastatic lesions differed significantly between males (8.1 months) and females (11.7 months) (p=0.009). Conclusions: The average latency period for new metastases is approximately 10 months. Our data suggests that males develop new metastatic tumors at a faster rate.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".