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Record W2954311990 · doi:10.14740/wjon1210

Non-Muscle Invasive Bladder Cancer: A Review of the Current Trend in Africa

2019· review· en· W2954311990 on OpenAlexvenueno aff
Ayun Cassell, Bashir Yunusa, Mohamed Jalloh, Mouhamadou M. Mbodji, Abdourahmane Diallo, Madina Ndoye, Yoro Diallo, Issa Labou, Lamine Niang, Sérigne Maguèye Gueye

Bibliographic record

VenueWorld Journal of Oncology · 2019
Typereview
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCystectomyBladder cancerCancerOncologyIncidence (geometry)UrologyInternal medicine

Abstract

fetched live from OpenAlex

Bladder cancer is the fourth most common cancer in men and the 11th most common cancer in woman accounting for 6.6% of all cancer cases. Approximately 70-75% bladder cancers are non-muscle invasive bladder cancer (NMIBC). A few African studies have provided considerable rates of NMIBC as compared to western settings 70% to 85%. Critical step in the management of NMIBC is to prevent tumor recurrence which include transurethral resection of the bladder tumor (TURBT) for staging and histological diagnosis. A second TURBT for high grade tumor, T1 tumors and intravesical adjuvant chemotherapy and immunotherapy are essential to reduce recurrence rate. Nevertheless, variant histology, multiple, progressive and recurrent high-grade tumors are best treated with early radical cystectomy. The African literature is scanty on the management of NMIBC. Most of the histological types are squamous cell bladder cancer and may not conform to transurethral resection only but rather radical cystectomy. Most of these patients are not suitable for any form of treatment as they present with advanced disease. However, there is an increasing incidence of urothelial cancer in Africa over the years due to urbanization. It is best that major investment is made in uro-oncological care to address the growing challenge of these subtypes.

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 score0.914
Threshold uncertainty score0.999

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.094
GPT teacher head0.411
Teacher spread0.318 · 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

Citations68
Published2019
Admission routes1
Has abstractyes

Explore more

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