Study of the Prevalence and the Incidence of the Prostate Cancer in the North-Cameroon: Means and Costs of Management
Bibliographic record
Abstract
Introduction: The high mortality rate of prostate cancer in Cameroon, its high incidence, its prevalence, the lack of epidemiological data for the north which for the case is the poorest area of the country led us to conduct this study with for the purpose of presenting the epidemiological, clinical and para-clinical aspects, the cost and means of management with a view to setting up adequate management policies. Patients and Methods: We conducted a cross-sectional analytical study in the city of Ngaoundéré for a period of 5months. The data were obtained after a survey of patients and collection of results from the pathology registry of the Islamic clinic of Adamawa three months before the start of our study. Results: The prevalence and incidence of prostate cancer were 28.7% and 24.32%, respectively. The average age of prostate cancer patients was 66.5 years. Gleason scores were less than 6 in 44.44% of cases. Risk factors related to familial cancer cases were difficult to determine. The symptoms were dominated in patients by urinary retention associated with polyuria, dysuria and pollakiuria. The means and costs of care were scalable depending on the difficulty of achieving the technique.Conclusion: Prostate cancer remains a real health problem in the north because of its incidence and high prevalence and requires the implementation of a government policy of care.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".