Predictors of treatment attrition in patients with metastatic colorectal cancer (mCRC).
Bibliographic record
Abstract
e18041 Background: Although the treatment landscape of cancer has evolved significantly with the introduction of novel and more efficacious drugs, the positive impact of these new therapies may be limited by attrition and ultimately non-exposure to later lines of therapy. Using a population-based cohort of mCRC, our aims were to characterize rates of attrition and determine factors associated with failure to receive each line of treatment. Methods: Medical records of patients who were diagnosed with mCRC from 2008-10 and referred to any 1 of 5 cancer centers in British Columbia were merged with systemic therapy data from the provincial pharmacy database. We classified patients into mutually exclusive treatment categories: a) receipt of all available lines of mCRC therapies; b) attrition directly attributable to disease, such as cancer progression or death; c) attrition attributable to other clinical factors, including toxicity, and d) attrition secondary to non-clinical factors, including personal/social circumstances. Multivariate logistic regression models were constructed to identify predictors. Results: We identified 525 eligible mCRC patients: median age 64 years, 57% men, 55% Caucasian, 68% ECOG 0/1, 41% and 35% never and ever smokers, respectively. The attrition rate was 40% (95% confidence interval [95% CI], 36%-44%) for first line treatment, 25% (95% CI, 19%-31%) for second line treatment and 14% (95% CI, 5.5%-22.5%) for third line. While cancer progression (31%) and chemo toxicity (30%) were the most common causes of attrition, other frequent reasons included death (20%) and patient preference (14%). On multivariable analysis, first-line treatment attrition was associated with worse baseline ECOG (odds ratio [OR], 1.92; p < 0.001) and older age at diagnosis of mCRC (OR, 1.04; p < 0.001). When we examined attrition over all lines, it was significantly correlated with worse ECOG (OR, 2.44; p < 0.001). Conclusions: Treatment attrition is a prevalent problem in mCRC and can hinder the benefits that would otherwise be possible with a sequential treatment algorithm. Some causes of attrition are potentially modifiable and may reflect opportunities for patients to maximize exposure to all lines of therapies.
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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 | 0.002 | 0.010 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".