Predictive Factors of the Use of Systemic Therapy in Stage IV Colorectal Cancer: Who Gets Chemotherapy?
Bibliographic record
Abstract
BACKGROUND: Chemotherapy improves survival in patients with stage IV colorectal cancer (CRC). Although in a clinical trial setting, strict eligibility criteria are used for chemotherapy, little is known about the use of chemotherapy in the general population. The study aims to assess clinicopathological variables that correlate with the use of chemotherapy in patients with stage IV CRC. METHODS: A retrospective cohort study involving patients with stage IV CRC, diagnosed between 1992 and 2005, in the province of Saskatchewan was carried out. A logistic regression analysis was performed to assess the correlation of various clinicopathological factors with the use of chemotherapy. RESULTS: A total of 1,237 eligible patients were identified. Their median age was 70 years (range: 22-98) and the male:female ratio was 1.3:1. 23.8% had an ECOG performance status (PS) of ≥2 and 61.8% of the patients had a comorbid illness. 46.8% of the patients received chemotherapy. The multivariate logistic regression analysis revealed that an age of <65 years [odds ratio (OR) 3.82, 95% CI: 2.59-5.63], metastasectomy (OR 3.60, 95% CI: 1.82-7.10), normal albumin (OR 3.26, 95% CI: 2.44-4.36), no comorbid illness (OR 2.87, 95% CI: 1.34-6.16), ECOG PS of <2 (OR 2.72, 95% CI: 1.94-3.82), normal blood urea nitrogen (OR 2.24, 95% CI: 1.40-3.59), palliative radiation (OR 2.03, 95% CI: 1.38-2.99), primary tumor resection (OR 2.00, 95% CI: 1.47-2.73), and the time period (OR 1.85, 95% CI: 1.41-2.42) were significantly correlated with the use of chemotherapy. CONCLUSIONS: The use of chemotherapy appears to be increasing in stage IV CRC. Patients treated with curative intention or who underwent primary tumor resection were more likely to receive chemotherapy. Despite a known benefit of chemotherapy in elderly patients, a differential use of chemotherapy was noted in this 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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".