Predictors of attrition in the treatment of metastatic colorectal cancer (MCRC).
Bibliographic record
Abstract
e17734 Background: The treatment landscape for many cancers has evolved significantly with the introduction of novel and more efficacious agents. However, the positive impact of these new therapies on outcomes may be limited by patient attrition if individuals are not exposed to later lines of treatment. 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 to 2009 and referred to any 1 of the 5 regional 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 treatments; b) attrition directly attributable to disease, such as cancer progression or death; and c) attrition secondary to other clinical factors, including toxicities. Multivariate logistic regression models with all-cause attrition as the main outcome were constructed to identify predictors, while adjusting for potential confounders. Results: We identified 302 eligible MCRC patients with complete records: median age was 65 years, 55% were men, 40% were White, 53% were ECOG 0/1, and 41% and 59% were never and ever smokers, respectively. In the entire cohort, only 60 (19%) patients received all lines of MCRC therapies. While worsening disease and death represented the most common cause (40%), other frequent causes of attrition included severe toxicities from prior or ongoing therapies (22%), patient preference (10%), decline in functional status (12%), and presence of significant comorbidities. In multivariate analyses, never smokers (p = 0.01) and individuals with baseline ECOG 2+ (p = 0.01) were significantly more likely to experience treatment attrition. Conclusions: Treatment attrition is a prevalent problem in MCRC and can hinder the benefit of treatment algorithms that rely on a temporal sequence of effective therapies. Some causes of attrition are potentially modifiable and may reflect opportunities for patients to maximize exposure to all lines of therapies.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".