Survival outcomes associated with completion of adjuvant oxaliplatin‐based chemotherapy for stage <scp>III</scp> colon cancer: A national population‐based study
Bibliographic record
Abstract
The impact of cycle completion rates of oxaliplatin-based adjuvant chemotherapy for stage III colon cancer in real-world practice is unknown. We assessed its impact, and that of treatment modification, on 3-year cancer-specific mortality. Four thousand one hundred and forty-seven patients with pathological stage III colon cancer undergoing major resection from 2014 to 2017 in the English National Health Service were included. Chemotherapy data came from linked national administrative datasets. Competing risk regression analysis for 3-year cancer-specific mortality was performed according to completion of <6, 6-11, or 12 5-fluoropyrimidine and oxaliplatin (FOLFOX) cycles, or <4, 4-7, or 8 capecitabine and oxaliplatin (CAPOX) cycles, adjusted for patient, tumour and hospital-level characteristics. Median age was 64 years. Thirty-two per cent of patients had at least one comorbidity. Forty-two per cent of patients had T4 disease, and 40% had N2 disease. Compared to completion of 12 FOLFOX cycles, cancer-specific mortality was higher in patients completing <6 cycles [subdistribution hazard ratios (sHR) 2.17; 95% CI 1.56-3.03] or 6-11 cycles (sHR 1.40; 95% CI 1.09-1.78) (P < .001). Compared to completion of 8 CAPOX cycles, cancer-specific mortality was higher in patients completing <4 cycles (sHR 2.02; 95% CI 1.53-2.67) or 4-7 cycles (sHR 1.63; 95% CI 1.27-2.10) (P < .001). Dose reduction and early oxaliplatin discontinuation did not impact mortality in patients completing all cycles. Completion of all cycles of chemotherapy was associated with improved cancer-specific survival in real-world practice. Poor prognostic factors may have affected findings, however, patients completing <50% of cycles had poor outcomes. Clinicians may wish to facilitate completion with treatment modification in those able to tolerate it.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".