Development of a comprehensive and sustainable gynecologic oncology training program in western Kenya, a low resource setting
Bibliographic record
Abstract
To provide information on the development of a gynecologic oncology training program in a low-resource setting in Kenya. This is a review of a collaboration between Kenyan and North American physicians who worked together to develop a gynecologic oncology training in Kenya. We review the published data on the increase of cancer incidence in sub-Saharan Africa and outline the steps that were taken to develop this program. The incidence of cervical cancer in Kenya is very high and is the leading cause of cancer mortality in Kenya. WHO identifies cancer as a new epidemic affecting countries in sub-Saharan Africa. In Kenya, a country of 45 million, there is limited resources to diagnose and treat cancer. In 2009 in western Kenya, at Moi University there was no strategy to manage oncology in the Reproductive Health department. There was only 1 gynecologic oncologists in Kenya in 2009. A collaboration between Canadian and Kenya physicians resulted in development of a gynecologic oncology clinical program and initiation of fellowship training in Kenya. In the past 4 years, five fellows have graduated from a 2 year fellowship training program. Integration of data collection on all the patients as part of this program provided opportunities to do clinical research and to acquire peer reviewed grants. This is the first recognized fellowship training program in sub-Saharan Africa outside of South Africa. It is an example of a collaborative effort to improve women's health in a low-resource country. This is a Kenyan managed program through Moi University. These subspecialty trained doctors will also provide advice that will shape health care policy and provide sustainable expertise for women diagnosed with a gynecologic cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".