Association of Weight Change after Colorectal Cancer Diagnosis and Outcomes in the Kaiser Permanente Northern California Population
Bibliographic record
Abstract
Abstract Background: Higher body mass index (BMI) is associated with incident colorectal cancer but not consistently with colorectal cancer survival. Whether weight gain or loss is associated with colorectal cancer survival is largely unknown. Methods: We identified 2,781 patients from Kaiser Permanente Northern California diagnosed with stages I–III colorectal cancer between 2006 and 2011 with weight and height measurements within 3 months of diagnosis and approximately 18 months after diagnosis. We evaluated associations between weight change and colorectal cancer–specific and overall mortality, adjusted for sociodemographics, disease severity, and treatment. Results: After completion of treatment and recovery from stage I–III colorectal cancer, loss of at least 10% of baseline weight was associated with significantly worse colorectal cancer–specific mortality (HR 3.20; 95% confidence interval [CI], 2.33–4.39; Ptrend < 0.0001) and overall mortality (HR 3.27; 95% CI, 2.56–4.18; Ptrend < 0.0001). For every 5% loss of baseline weight, there was a 41% increased risk of colorectal cancer–specific mortality (95% CI, 29%–56%). Weight gain was not significantly associated with colorectal cancer–specific mortality (Ptrend = 0.54) or overall mortality (Ptrend = 0.27). The associations were largely unchanged after restricting analyses to exclude patients who died within 6 months and 12 months of the second weight measurement. No significant interactions were demonstrated for weight loss or gain by gender, stage, primary tumor location, or baseline BMI. Conclusions: Weight loss after diagnosis was associated with worse colorectal cancer–specific mortality and overall mortality. Reverse causation does not appear to explain our findings. Impact: Understanding mechanistic underpinnings for the association of weight to worse mortality is important to improving patient outcomes. Cancer Epidemiol Biomarkers Prev; 26(1); 30–37. ©2016 AACR. See all the articles in this CEBP Focus section, “The Obesity Paradox in Cancer: Evidence and New Directions.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".