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 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".