A blueprint for creating an early-onset colorectal cancer program based on experiences from seven clinical centers across the United States and Canada
Bibliographic record
Abstract
Abstract Background The incidence of individuals diagnosed with colorectal cancer under the age of 50, often referred to as early onset colorectal cancer (EOCRC), has risen worldwide. The EOCRC patients have unique needs however, there are no clear guidelines on how to address them. The objective of the present study was to propose a blueprint for EOCRC program development and implementation. Materials and Methods A consensual qualitative design was used to frame the present study. Seven leaders of North American EOCRC centers were invited to devise a stepwise blueprint for developing and implementing EOCRC programs. Discussions were transcribed and conceptually organized into a framework. An analysis of documents detailing and describing the services offered within each centre was as well conducted. Results The analysis was organized around two domains: (a) the rationale underlying the creation of EOCRC programs, and (b) the lessons learned from developing and implementing EOCRC programs. Participants agreed that as the EOCRC programs represent innovative initiatives, a program manager who closely monitors the program is core to successful implementation. Conclusion This study outlines a 10 step actionable guideline for developing and implementing EOCRC programs aiming at addressing the unmet needs of this growing population. Implications for Practice The rising incidence of EOCRC calls for specialized programs tailored to respond to the unique medical and non-medical needs of this population. The proposed blueprint, made of 10steps detailing resources, challenges, opportunities, and actions leading to a successful EOCRC program development and implementation, was meant to contribute to this process.
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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 | 0.041 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| 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".