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Record W4411399583 · doi:10.1200/go-24-00459

Burden of General Surgical Cancer Care in West Africa: A Review of the Literature

2025· review· en· W4411399583 on OpenAlexaffabout
Nikita Arora, Linda Yi Ning Fei, Matthew Jalink, Olusegun Isaac Alatise, Gregory Knapp, Faizal Haji, Emmanuel Ezeome, Sulaiman Nanji

Bibliographic record

VenueJCO Global Oncology · 2025
Typereview
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsBC Children's HospitalDalhousie UniversityKingston Health Sciences CentreUniversity of British ColumbiaQueen's University
Fundersnot available
KeywordsMedicineWorkforceContext (archaeology)CancerGeneral partnershipHealth careCurriculumFamily medicineEconomic growthBusinessGeographyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: The global burden of cancer is growing rapidly, with a disproportionately higher increase in low- and middle-income countries. West Africa is particularly affected by this rise, where cancer control systems are woefully inadequate to meet the increasing needs of patients. Although many gaps exist across the continuum of cancer care, perhaps the most striking is the lack of surgical services, which plays a vital role in up to 80% of all patients with cancer. To address this critical gap in cancer care, the West African College of Surgeons established a bilateral partnership with Queen's University, Canada, to grow the surgical oncology workforce for the region by cocreating and implementing a general surgical oncology fellowship training program. METHODS: To inform the design of the curriculum tailored to the cancer care needs of West Africa, a narrative review of the literature was performed to identify the incidence and mortality associated with general surgical cancers in the region, as well as the health care resources available to address these malignancies. RESULTS: This comprehensive report provides a contemporary understanding of the landscape of cancer care with respect to the burden of disease, the existing resources, and the challenges in delivery of cancer services for West Africa. CONCLUSION: The findings in this report quantify the unmet demand for cancer care in West Africa and highlight the scope for context-specific cancer training in this region.

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: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score0.835

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.020
GPT teacher head0.456
Teacher spread0.436 · 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
GenreReview

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
Published2025
Admission routes2
Has abstractyes

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