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Record W4412755653 · doi:10.4314/ahs.v25i1.27

Pattern of gastrointestinal malignancies in a suburban centre in Southern Nigeria

2025· article· en· W4412755653 on OpenAlexaff
Esteem Tagar, DO Irabor, James Kpolugbo, Clifford Ikhuoria Owobu, Ifeanyichukwu Michael Chukwu

Bibliographic record

VenueAfrican Health Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsAmbrose University
Fundersnot available
KeywordsMedicineEnvironmental healthSocioeconomicsFamily medicine

Abstract

fetched live from OpenAlex

Background: Recent studies in sub-Saharan Africa have suggested an increasing incidence of gastrointestinal malignancies which consequently poses significant public health burden in terms of morbidity and mortality. Objective: This study was carried out to assess the distribution, clinical presentation, and histopathological characteristics of gastrointestinal malignancies in a tertiary health centre in Southern Nigeria. Methods: A retrospective review of all patients with histologic diagnosis of gastrointestinal malignancy in a tertiary health institution in Southern Nigeria between January 2013 and December 2022. Results: A total of 104 patients were included in the study. There were 64 males and 40 females with a male to female ratio of 1.6:1 and the peak age group was 41-50 years. The commonest sites affected were the colon and rectum (63.5%), followed by the stomach (22.1%). Adenocarcinoma was the predominant type of gastrointestinal malignancy, comprising 87.5% of the cases with most of them well differentiated. Others included sarcoma (6.7%), squamous cell carcinoma (1.9%), neuroendocrine tumour (1.9%), lymphoma (1%), and plasmacytoma (1%). Conclusion: Colon and rectal cancers were the predominant gastrointestinal malignancies with a male preponderance, and individuals between 41-50 years, who constitute the bulk of the country's workforce, were more affected. It is imperative to develop strategies aimed at reducing the incidence and fatality.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.299
Teacher spread0.277 · 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 designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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