Comorbidity prevalence and incidence in cancer survivors: a longitudinal All of Us study
Bibliographic record
Abstract
BACKGROUND: Comorbidities worsen cancer survival, but patterns of preexisting and new-onset comorbidities among cancer survivors are unknown. METHODS: We investigated self-reported and clinically diagnosed comorbidity among cancer survivors in the All-of-Us program's national database. Eight highly prevalent comorbidities were identified using self-reported data from the personal health history survey among cancer survivors (n = 20 534) and noncancer adults (n = 113 628) and validated among cancer survivors (n = 26 978) using data from electronic health records (EHRs). Among 5-year survivors (n = 9174) documented in EHR, we further estimated the incidence of new-onset comorbidities. RESULTS: The most prevalent comorbidities identified in personal health history data were hypertension (40.5%), osteoarthritis (28.4%), depression (28.0%), and obesity (23.2%). EHR data identified preexisting comorbidities: hypertension (43.3%), osteoarthritis (29.4%), depression (19.4%), and obesity (19.1%). During 5-year survival, more than 50% of cancer survivors developed at least one new comorbidity, and more than 25% developed two or more. The onset of new comorbidities showed a sharp increase in the first-year postdiagnosis. Incidence rates varied by age, race, and ethnicity. CONCLUSION: Future research is needed to develop effective strategies to prevent new-onset comorbidities during and after cancer treatment.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".