Conditional Survival after a Diagnosis of Malignant Brain Tumour in Canada: 2000–2008
Bibliographic record
Abstract
BACKGROUND: "Conditional survival probability" is defined as the probability that a patient will survive an additional time, given that the patient has already survived a defined period of time after diagnosis. Such estimates might be more relevant for clinicians and patients during post-diagnosis care, because survival probability projections are based on the patient's survival to date. Here, we provides the first population-based estimates of conditional survival probabilities by histology for brain cancer in Canada. METHODS: Canadian Cancer Registry data were accessed for patients diagnosed with primary brain cancers during 2000-2008. Kaplan-Meier survival probabilities were estimated by histology. Conditional survival probabilities at 6 months (short-term, denoted scs) and 2 years (long-term, denoted lcs) were derived from the Kaplan-Meier survival estimates for a range of time periods. RESULTS: Among the 20,875 patients who met the study criteria, scs increased by a margin of 16-18 percentage points from 6-month survivors to 2-year survivors for the three most aggressive brain cancers. The lcs for 2-year survivors was 66% or greater for all tumour groups except glioblastoma. The lcs for 4-year survivors was 62% or greater for all histologies. For glioblastoma and diffuse astrocytoma, the lcs increased each year after diagnosis. For all other histologies, the lcs first increased and then plateaued from 2 years after diagnosis. The lcs and scs both worsened with increasing older age at diagnosis. SUMMARY: We report histologically specific conditional survival probabilities that can have value for clinicians practicing in Canada as they plan the course of follow-up for individual patients with brain cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".