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Record W4403763941 · doi:10.1080/14796694.2024.2416885

Colorectal cancer research priorities in Uganda: perspectives from local key experts and stakeholders

2024· article· en· W4403763941 on OpenAlexfundno aff
Nicholas Matovu, Noleb M. Mugisha, Alfred Jatho, Charlene M. McShane

Bibliographic record

VenueFuture Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsnot available
FundersQueen's UniversityQueen's University Belfast
KeywordsMedicineKey (lock)Colorectal cancerCancerEnvironmental resource managementInternal medicineComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.073
GPT teacher head0.388
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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