Stroke impact on mortality and psychologic morbidity within the Childhood Cancer Survivor Study
Bibliographic record
Abstract
BACKGROUND: Poor socioeconomic and health-related quality of life (HRQOL) outcomes in survivors of childhood cancer can lead to distress and overall negatively impact the lives of these individuals. The current report has highlighted the impact of stroke and stroke recurrence on mortality, psychological HRQOL, and socioeconomic outcomes within the Childhood Cancer Survivor Study (CCSS). METHODS: The CCSS is a retrospective cohort study with longitudinal follow-up concerning survivors of pediatric cancer who were diagnosed between 1970 and 1986. Mortality rates per 100 person-years were calculated across 3 periods: 1) prior to stroke; 2) after first stroke and before recurrent stroke; and 3) after recurrent stroke. Socioeconomic outcomes, the standardized Brief Symptoms Inventory-18, the Medical Outcomes Study 36-Item Short Form Health Survey, and the CCSS-Neurocognitive Questionnaire also were assessed. RESULTS: Among 14,358 participants (median age, 39.7 years), 224 had a stroke after their cancer diagnosis (single stroke in 161 patients and recurrent stroke in 63 patients). Based on 2636 deaths, all-cause late mortality rates were 0.70 (95% CI, 0.68-0.73) prior to stroke, 1.03 (95% CI, 0.73-1.46) after the first stroke, and 2.42 (95% CI, 1.48-3.94) after the recurrent stroke. Among 7304 survivors, those with stroke were more likely to live with a caregiver (single stroke odds ratio [OR], 2.3 [95% CI, 1.4-3.8]; and recurrent stroke OR, 5.3 [95% CI, 1.7-16.8]) compared with stroke-free survivors. Stroke negatively impacted task efficiency (single stroke OR, 2.4 [95% CI, 1.4-4.1] and recurrent stroke OR, 3.3 [95% CI, 1.1-10.3]) and memory (single stroke OR, 2.1 [95% CI, 1.2-3.7]; and recurrent stroke OR, 3.5 [95% CI, 1.1-10.5]). CONCLUSIONS: Stroke and stroke recurrence are associated with increased mortality and negatively impact HRQOL measures in survivors of pediatric 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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".