Effect of comorbidities on outcomes in colorectal cancer (CRC) survivors.
Bibliographic record
Abstract
10055 Background: The effect of comorbidities on outcomes in cancer survivors has not been evaluated in detail, but this can inform survivorship care. We aimed to evaluate comorbid medical conditions, causes of death (COD), and the effect of these conditions on survival among CRC survivors in a Canadian province. Methods: A population-based cohort study using administrative data. Patients were diagnosed with stage I-III CRC from 2004 to 2015. ICD-10 codes were used to categorize COD. CRC patients were divided into 5 mutually exclusive comorbid groups: cardiovascular disease (CVD), diabetes (DM), both cardiovascular disease and diabetes (CVD+DM), other comorbidities (OC), and no comorbidities. Kaplan Meier and Cox proportional hazards models were used to evaluate survival, adjusting for age, cancer stage, and treatment. Results: We evaluated 12,265 patients. Median age 67.3(range 18-104) years, 56.2% men, 61.8% colon cancer, 38.8% stage III disease, and 36.8% Charlson comorbidity index ≥1. There were 1153 (9.4%), 1711(13.9)%, 515(4.2%), and 1141(9.3%) patients in the CVD, DM, CVD+DM and OC groups, respectively. Mean follow-up was 3.8 years. Median overall survival (mOS) was 8.6 (CI 8.3-8.9) years in the entire cohort. Among those who died (N = 3964), 51.2% and 39.3% were due to CRC and other causes, respectively. CVD was a common non-CRC COD (17.1%). In comparison to those with no comorbidities, patients with CVD+DM fared the worst (mOS 3.3 [2.8-3.7] years; adjusted HR for death, 2.27, 95% CI 2.0-2.6, p < 0.001) (see Table). For stage III disease, the percentage receiving curative intent (surgery + adjuvant) treatment was different (p < .001) across groups (31.7% in CVD+DM, 37.6% in CVD, 66.7% in DM, and 57.4% in OC). Conclusions: Specific comorbid medical conditions are associated with increased risk of death from CRC and non-CRC causes. Undertreatment was associated with comorbidity profile and may be a driver of worse CRC survival in these patients. Engagement of primary care and other specialty providers earlier in the survivorship trajectory is warranted. Group mOS HR (95% CI) P value No comorbidities 10.8 yrs referent CVD 4.2 yrs 1.98 (1.7-2.1) < .001 DM 7.3 yrs 1.33 (1.2-1.5) < .001 CVD+DM 3.3 yrs 2.27 (2.0-2.6) < .001 OC 6.0 yrs 1.61 (1.5-1.8) < .001
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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".