Colorectal cancer research priorities in Uganda: perspectives from local key experts and stakeholders
Bibliographic record
Abstract
The incidence of colorectal cancer (CRC) is increasing in Uganda but there is limited local research to guide policy and programming for CRC prevention and control. A stakeholder engagement workshop took place in Kampala on 19 March 2024 to identify challenges and opportunities for CRC prevention and control in Uganda. A total of 30 stakeholders with expertise in CRC primary and secondary prevention, diagnosis, treatment, palliative care as well as cancer survivors participated in the workshop. Key challenges for primary prevention included low knowledge/awareness of CRC among the general population and health workers, and rising prevalence of CRC related risk factors. Limited CRC screening, diagnostic facilities and specialists were identified as barriers to diagnosis. Treatment related challenges included limited accessibility to surgical services and drugs, late-stage presentation leading to poor treatment response, treatment abandonment and drug related toxicity. Lack of universal health coverage policies, limited community-based cancer awareness programs, and lack of national cancer registries were cited as policy and economics challenges. Opportunities to address these challenges were discussed. Our findings highlight areas for further research and prioritization to address Uganda's growing CRC burden and may be applicable to other low-resource settings.
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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 | 0.033 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".