Critical research gaps and recommendations to inform research prioritisation for more effective prevention and improved outcomes in colorectal cancer
Bibliographic record
Abstract
Objective Colorectal cancer (CRC) leads to significant morbidity/mortality worldwide. Defining critical research gaps (RG), their prioritisation and resolution, could improve patient outcomes. Design RG analysis was conducted by a multidisciplinary panel of patients, clinicians and researchers (n=71). Eight working groups (WG) were constituted: discovery science; risk; prevention; early diagnosis and screening; pathology; curative treatment; stage IV disease; and living with and beyond CRC. A series of discussions led to development of draft papers by each WG, which were evaluated by a 20-strong patient panel. A final list of RGs and research recommendations (RR) was endorsed by all participants. Results Fifteen critical RGs are summarised below: RG1 : Lack of realistic models that recapitulate tumour/tumour micro/macroenvironment; RG2 : Insufficient evidence on precise contributions of genetic/environmental/lifestyle factors to CRC risk; RG3 : Pressing need for prevention trials; RG4 : Lack of integration of different prevention approaches; RG5 : Lack of optimal strategies for CRC screening; RG6 : Lack of effective triage systems for invasive investigations; RG7 : Imprecise pathological assessment of CRC; RG8 : Lack of qualified personnel in genomics, data sciences and digital pathology; RG9 : Inadequate assessment/communication of risk, benefit and uncertainty of treatment choices; RG10 : Need for novel technologies/interventions to improve curative outcomes; RG11 : Lack of approaches that recognise molecular interplay between metastasising tumours and their microenvironment; RG12 : Lack of reliable biomarkers to guide stage IV treatment; RG13 : Need to increase understanding of health related quality of life (HRQOL) and promote residual symptom resolution; RG14 : Lack of coordination of CRC research/funding; RG15 : Lack of effective communication between relevant stakeholders. Conclusion Prioritising research activity and funding could have a significant impact on reducing CRC disease burden over the next 5 years.
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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 | 0.417 | 0.570 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".