Patterns of systemic therapy in metastatic colorectal cancer (mCRC): Use of intermittent and stop-and-go strategies in a population-based setting.
Bibliographic record
Abstract
644 Background: The concept of treatment to disease progression must be tempered by the potential for cumulative toxicities of ongoing therapy. Data suggest that treatment holidays may provide improved quality of life without significantly compromising outcomes in metastatic disease. Our aim was to characterize the frequency of intermittent and stop-and-go strategies in a population-based cohort of mCRC patients. Methods: Patients diagnosed with mCRC and who received any palliative systemic treatment at the British Columbia Cancer Agency from Jan 2008 to Dec 2010 were identified from the provincial pharmacy database. First-line (1L) and second-line (2L) therapy choices, duration on each line of therapy, and number of treatment holidays, defined as any time interval of >/= 4 weeks without any therapy, were characterized. Results: In total, 946 patients were identified: median age was 64 (IQR 55-72) years, 521 (55%) were men, and 626 (66%) had colon cancer. In 1L treatment, the majority received combination systemic therapy: 474 (50%) FOLFIRI +/- bevacizumab (B), 224 (24%) FOLFOX +/- B, and 248 (26%) 5-FU/capecitabine. The median number of cycles on 1L therapy was 9 (IQR 4-13). Treatment holidays were common with 302 (32%) patients experiencing at least one break, each lasting a median of 49 (IQR 36-99) days. Compared with no B, 1L patients treated with B received more therapy (median 12 [IQR 8-20] vs 8 [IQR 4-12] cycles). However, they were also more likely to undergo a treatment holiday (34% vs 31%), but the duration of each break was shorter (median 49 [IQR 36-92] vs 56 [IQR 35-119] days). Only 312 (33%) out of the 946 patients proceeded to 2L therapy. The median interval between stopping 1L and starting 2L was 43 (IQR 20-162) days. Compared with 1L, the median number of treatment cycles was shorter in 2L (median 7 [IQR 4-12]). Treatment holidays were also less frequent with only 50 (16%) out of 312 patients experiencing at least one break, each lasting a median of 56 (IQR 38-100) days. Conclusions: The use of the stop-and-go approach was prevalent in this population-based cohort of mCRC patients. The relationship between intermittent treatment and outcomes will be presented at the meeting.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".