Successes and challenges of establishing a cervical cancer screening and treatment program in western Kenya
Bibliographic record
Abstract
OBJECTIVE: To describe the challenges and successes of integrating a public-sector cervical screening program into a large HIV care system in western Kenya. METHODS: The present study was a programmatic description and a retrospective chart review of data collected from a cervical screening program based on visual inspection with acetic acid (VIA) between June 2009 and October 2011. RESULTS: In total, 6787 women were screened: 1331 (19.6%) were VIA-positive, of whom 949 (71.3%) had HIV. Overall, 206 women underwent cryotherapy, 754 colposcopy, 143 loop electrical excision procedure (LEEP), and 27 hysterectomy. Among the colposcopy-guided biopsies, 27.9% had severe dysplasia and 10.9% had invasive cancer. There were 68 cases of cancer, equating to approximately 414 per 100000 women per year. Despite aggressive strategies, the overall loss to follow-up was 31.5%: 27.9% were lost after a positive VIA screen, 49.3% between biopsy and LEEP, and 59.6% between biopsy and hysterectomy/chemotherapy. CONCLUSION: The established infrastructure of an HIV treatment program was successfully used to build capacity for cervical screening in a low-resource setting. By using task-shifting and evidence-based, low-cost approaches, population-based cervical screening in a rural African clinical network was found to feasible; however, loss to follow-up and poor pathology infrastructure remain important obstacles.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".