Does the timing of comorbidity affect colorectal cancer survival? A population based study
Bibliographic record
Abstract
OBJECTIVES: Comorbid conditions in colorectal cancer patients can influence both clinical eligibility for treatment and survival. We aimed to evaluate the effect of comorbidity on 1 year survival from colorectal cancer, and to assess whether this effect varied with the timing of the comorbidity in relation to the cancer diagnosis. STUDY DESIGN AND SETTING: A population based cohort of 29,563 colorectal cancer patients diagnosed between 1997 and 2004 in the North West of England was evaluated. The excess hazard of death up to 1 year after diagnosis was estimated using deprivation and region specific life tables to adjust for background mortality. Results were adjusted for age and stage at diagnosis. RESULTS: Comorbid conditions diagnosed during the period 18 to 6 months before the diagnosis of colorectal cancer were strongly associated with lower survival at 1 year. Stage and age remained the strongest predictors of cancer related mortality even after adjustment for comorbidity. CONCLUSIONS: Administrative data provide a good estimate of the prevalence of most comorbid conditions but may be biased for some comorbid conditions that can be contra-indicators for cancer treatment. The time window in which a comorbid condition occurs in relation to the cancer diagnosis should be taken into account. Adjustment should be carried out, where possible, to provide more robust and clinically appropriate comparisons of population based cancer patient survival.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".