Challenges That Hinder the Translation of Clinical Advances Into Practice: Results From an International Assessment in Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: Over the past decade, individualization of treatment for colorectal cancer (CRC) has been improved by: (1) approval of several new agents by national agencies such as the US Food and Drug Administration (FDA); and (2) rapid advances in mutation analysis. However, data are sparse on the clinical challenges experienced by oncologists as they address the increased complexity created by the growing potential for individualization of CRC treatment. MATERIALS AND METHODS: To identify clinical challenges experienced by oncologists regarding CRC treatment, an international assessment was conducted. A mixed methods approach was used, with the collection and analysis of qualitative (semistructured telephone interviews) and quantitative (online survey) data. Participants were oncologists actively practicing in 1 of 7 targeted countries with a minimum caseload of 10 CRC patients per year. RESULTS: The sample included 358 oncologists from China (n = 68), France (n = 44), Germany (n = 44), Italy (n = 45), Spain (n = 44), the United Kingdom (n = 45), and the United States (n = 68). Mixed methods findings indicated that oncologists' treatment selection is hindered by practice challenges in: (1) mutation analysis and subsequent adaptation of treatment; (2) optimal sequential use of treatment choices; (3) treatment individualization based on patient and tumor profile; (4) management of side effects and toxicities; (5) chemoresistance, cross-resistance, and combinations to overcome resistance; and (6) access to new emerging treatments. CONCLUSION: In the context of increased complexity created by the approval of new agents and advances in mutation analysis, challenges are experienced by practicing oncologists in the individualization of treatment for CRC patients. Details of these challenges should stimulate dialogue among oncologists, and development of interventions to improve clinical practice.
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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 | 0.084 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".