The Influence of Adjuvant Chemotherapy Dose Intensity on Five-Year Outcomes in Resected Colon Cancer: A Single Centre Retrospective Analysis
Bibliographic record
Abstract
There is evidence that achieving a dose intensity > 80% in adjuvant colon cancer treatment improves survival. In total, 192 consecutive patients with resected stage III and high-risk stage II colon cancer that received adjuvant chemotherapy were retrospectively analyzed. Patients who received at least 6 weeks of adjuvant therapy were included. The primary objective was to assess the influence of dose index (DI) and relative dose intensity (RDI) on DFS and OS at 3 and 5 years in patients receiving fluorouracil-based doublet therapy with oxaliplatin (FOLFOX) (5-FU and oxaliplatin assessed separately), or capecitabine monotherapy. In the capecitabine group, DFS rates for 3 and 5 years were 66.7% and 57.6%, respectively, while OS rates were 80.3% and 66.7%, respectively. Those who received FOLFOX had DFS rates of 76.9% and 71.2% at 3 and 5 years, respectively. OS rates were 86.4% and 76.7% at 3 and 5 years, respectively. Median RDI was 73.8% for capecitabine and 76.3% and 85.6% for the oxaliplatin and 5-FU components respectively. Based on a multivariate analysis in patients receiving FOLFOX, those with an oxaliplatin DI > 80% had improvements in DFS and OS compared to those with an oxaliplatin DI of ≤80%. Otherwise, there was no significant difference in DFS or OS when comparing patients who achieved an RDI or a DI of above versus below 80% in the patients receiving adjuvant chemotherapy for resected colon cancer.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".