Patterns of cancer in Needle Hospital, Hargeisa, Somaliland from July 2022 to June 2023
Bibliographic record
Abstract
Purpose: Globally, the incidence of and mortality from cancer is rapidly increasing and presents a barrier to increasing life expectancy. Based on regional and global trends, cancer incidence in Somaliland is expected to increase. Until recently, there was no dedicated cancer clinic in Somaliland. In July 2022, a medical oncology service was started in Needle Hospital, Hargeisa, Somaliland. This study reports on patterns of cancer with respect to patients' region, age, gender, comorbidities, site and subsites of cancer, histology and stages. Patients and method: A retrospective study was conducted to determine the patterns of cancer among patients evaluated in the Needle Hospital cancer clinic from July 2022 to June 2023. Data were extracted from the cancer patient registration file and charts. Descriptive statistics were applied using the Statistical Package for the Social Sciences version 23. Results: A total of 232 cancer patients were evaluated during the study period. The median age was 60.0 years. More than half (56.5%) of the patients were female, with a female-to-male ratio of 1.3:1. Most of the patients (66.8%) came from Morodijeh, followed by Togdher (15.1%) and Awdal (5.2%) regions. The most common anatomic subsites of the cancers were breast, esophageal and prostate cancers, accounting for 15.9%, 8.2% and 7.3%, respectively. Based on histology, adenocarcinoma and squamous cell carcinoma accounted for 42.2% and 25%, respectively. Most patients presented at an advanced stage; stage IV cancer accounted for 44.4% and stage III cancers accounted for 29.30% of the total patients. Conclusion: Based on this study, cancer is one of the emerging health problems in Somaliland. Most patients presented at an advanced stage. Breast, esophageal and prostate cancers were the most commonly diagnosed cancers. Esophageal cancer, being a common finding, is disparate, so a study investigating the aetiology and biology of esophageal cancer in Somaliland is recommended. We also recommend establishing the National Cancer Control Plan, a national cancer registry and developing research capacity. Finally, to improve cancer outcomes, capacity building in diagnostic and treatment facilities and regional and international collaboration should also be prioritised.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".