Clinical Predictors of Early Mortality in Colorectal Cancer Patients Undergoing Chemotherapy: Results From a Global Prospective Cohort Study
Bibliographic record
Abstract
Abstract Background Early mortality is a major problem in colorectal cancer (CRC). We have shown that Khorana Score is predictive of early mortality in other cancers. Here, we evaluated the value of this score and other prognostic variables in predicting early mortality in CRC. Methods CANTARISK was a prospective, noninterventional, global cohort study in patients with CRC initiating a new chemotherapy regimen. Data were collected at zero, two, four, and six months. Early mortality was defined as death within six months of enrollment. All data were compiled centrally and analyzed after the study closed. Statistically significant univariate associations were tested in multivariable models; adjusted odds ratios (ORs) are presented. Statistical tests were two-sided. Results From 2011 to 2012, 1789 CRC patients were enrolled. The median age was 62 years; 71% were Caucasian. One-third (35%) had a rectal primary, and 65% had metastatic disease. There were 184 (10.3%) patients who died during their first six months in the study. For low, intermediate, and high Khorana Score, there were 8.1%, 11.2% and 32.5% deaths, respectively. In multivariable analyses, Khorana Score was an independent predictor of early death (OR for high/intermediate vs low score = 1.70, P = .0027), in addition to age (OR for each incremental year = 1.03, P = .0014), presence of metastatic disease (OR = 3.28, P < .0001), and Easter Cooperative Oncology Group Performance Status Score of 2 or higher (OR = 3.85, P < .0001). Conclusions This study demonstrates that Khorana Score is predictive of early mortality in CRC patients. Intermediate- or high-risk patients, as defined by this score, may benefit from additional interventions aimed at reducing early mortality.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".