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Histo-Epidemiological Profile of Digestive Cancers in Togo

2015· article· en· W1719505078 on OpenAlexvenueno aff
Tchin Darré, Lantam Sonhaye, Mazamaesso Tchaou, K. Kanassoua, Abdoulatif Amadou, A Bagny, A. A. N’Guiessan, Koffi Amégbor, Gado Napo‐Koura

Bibliographic record

VenueJournal of cancer research updates · 2015
Typearticle
Languageen
FieldMedicine
TopicColorectal and Anal Carcinomas
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEsophagusStomachGastroenterologyAdenocarcinomaIncidence (geometry)CancerEpidemiologyRectumInternal medicineDigestive tractStomach cancerPathology

Abstract

fetched live from OpenAlex

Background:The frequency of digestive cancers is increasing since the last ten years. The aim of the study was to determine the epidemiologic and histologic aspects of digestive cancers in Togo. Method: We conducted a descriptive cross-sectional study focused on digestive cancers cases diagnosed from 1995 throughout 2014 (20 years) at the pathology laboratory of the Lomé teaching hospital. We included all digestive samples (biopsy, excision, surgical specimens) collected and sent to the pathology laboratory from 1994 to 2013 using data from the records of the laboratory. Results: We have collected 1306 cases of digestive cancers (20.4%). The annual incidence was 65.3 cases. The sex-ratio (M/F) was 1.5. The mean age was 47.8 years. The most common locations were the stomach (35.3%) and the esophagus (27.3%). The adenocarcinoma was predominant in the stomach (84.4%), the rectum (70.2%), and the colon (86.3%). The squamous cell carcinoma was commonly found at the esophagus (94.8%). The lymphomas were observed in the small intestine (53.4%). Conclusion: Digestive cancers are frequent in Togo and occur in young adults. The stomach cancer is the most common cancer

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.185
GPT teacher head0.462
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 source (direct Gemma or distilled Codex), 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

Citations2
Published2015
Admission routes1
Has abstractyes

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