Evaluation of Adjuvant Chemotherapy-Associated Steatosis (CAS) in Colorectal Cancer
Bibliographic record
Abstract
Chemotherapy-associated steatosis is poorly understood in the context of colorectal cancer. In this study, Stage II–III colorectal cancer patients were retrospectively selected to evaluate the frequency of chemotherapy-associated steatosis and to determine whether patients on statins throughout adjuvant chemotherapy develop chemotherapy-associated steatosis at a lower frequency. Baseline and incident steatosis for up to one year from chemotherapy start date was assessed based on radiology. Of 269 patients, 76 (28.3%) had steatosis at baseline. Of the remaining 193 cases, patients receiving adjuvant chemotherapy (n = 135) had 1.57 (95% confidence interval [CI], 0.89 to 2.79) times the adjusted risk of developing steatosis compared to patients not receiving chemotherapy (n = 58). Among patients who underwent chemotherapy, those using statins for pre-existing hyperlipidemia (n = 37) had 0.71 (95% CI, 0.10 to 2.75) times the risk of developing steatosis compared to patients who were not prevalent users of statins (n = 98). Chemotherapeutic treatment of Stage II–III colorectal cancer appears to be consistent with a moderately increased risk of steatosis, although larger studies are necessary to assess the significance of this observation. Prospective trials should be considered to further explore the potential for protective use of statins in this curative patient population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".