Non-Muscle Invasive Bladder Cancer: A Review of the Current Trend in Africa
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".