Comorbidity prevalence and incidence in cancer survivors: a longitudinal All of Us study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it