Overall and late mortality among 24 459 survivors of adolescent and young adult cancer in Alberta, Canada: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Adolescent and young adult (AYA) cancer survivors are at an increased risk of premature mortality due to their cancer and its treatment. Herein, we aimed to quantify the excess risks of mortality among AYA cancer survivors and identify target populations for intervention. METHODS: The Alberta AYA Cancer Survivor Study is a retrospective, population-based cohort of individuals diagnosed with a first primary neoplasm at age 15-39 years in Alberta, Canada, between 1983 and 2017. We assessed cancer survivors (ie, all individuals included in the cohort) overall and for 2-year and 5-year survivorship subpopulations. We calculated standardised mortality ratios and absolute excess risks (AERs; per 10 000 person-years) compared with the general population, and cumulative mortality probability. Causes of death were categorised as deaths due to recurrence or progression (of the first primary neoplasm), deaths due to a subsequent primary neoplasm (SPN), and deaths due to non-neoplastic causes. FINDINGS: Among the 24 459 individuals included in the cohort, 5916 deaths were observed, which was 11·4 times (95% CI 11·1-11·7) that expected for the general population, equating to 191·6 (186·2-196·9) excess deaths; correspondingly, 5-year survivors had 4·2 times (4·0-4·4) more deaths than expected, equating to 74·3 (69·8-78·8) excess deaths. Increased age at diagnosis, poorer neighbourhood income quintile at diagnosis, first primary neoplasm type, and initial treatment plan were identified as important risk factors for mortality. While recurrence or progression was the main cause of excess mortality (AER 172·2 [167·4-177·1]), the majority of deaths beyond 10 years from diagnosis were due to SPNs and non-neoplastic causes among survivors of endometrial cancer, testicular cancer, and Hodgkin lymphoma. The cumulative mortality probability significantly decreased among more recently diagnosed survivors for all-cause mortality (p<0·0001) as well as recurrence or progression deaths (p<0·0001) and SPN deaths (p=0·0070), suggesting that long-term survival is improving. INTERPRETATION: AYA cancer survivors have substantial excess mortality. Given the high burden of late SPN and non-neoplastic deaths, survivors of endometrial cancer, testicular cancer, and Hodgkin lymphoma are notable populations that might benefit from primary, secondary, and tertiary prevention strategies. FUNDING: None.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".