Premature Mortality Due to Hodgkin Lymphoma, Non-Hodgkin Lymphoma, Multiple Myeloma, and Leukemia in Canada: A Nationwide Analysis From 1980 to 2015
Bibliographic record
Abstract
Recently, we introduced a novel measure of "average life span shortened" (ALSS) to improve comparability of premature mortality over time. In this study, we applied this novel measure to examine trends in premature mortality caused by hematological cancers in Canada from 1980 to 2015. Mortality data for Hodgkin lymphoma, non-Hodgkin lymphoma, multiple myeloma, and leukemia were obtained from the World Health Organization mortality database. Years of life lost was calculated according to Canadian life tables. ALSS was defined as the ratio between years of life lost and expected life span. Over the study period, age-standardized rates of mortality decreased for all types of hematological cancers. Our new ALSS measure showed favorable trends in premature mortality for all types of hematological cancers among both sexes. For instance, men with non-Hodgkin lymphoma lost an average of 23.7% of their life span in 1980 versus 16.1% in 2015, while women with non-Hodgkin lymphoma lost an average of 21.7% of their life span in 1980 versus 15.5% in 2015. Results from this study showed that patients with hematological cancers experienced prolonged survival over a 35-year period although the magnitude of these life span gains varied by types of hematological cancers.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.001 | 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".