Utility of colposcopy for the screening and management of cervical cancer in Africa: a cross-sectional analysis of providers’ training and practices
Bibliographic record
Abstract
INTRODUCTION: Cervical cancer is a public health issue in Africa with devastating socioeconomic consequences due to the lack of organized screening programs. The success of screening programs depends on the appropriate investigation and management of women who test positive for screening. Colposcopic assessment following positive screening results is a noteworthy issue in Africa. This study aimed to assess the utilization of colposcopy by providers in the region. METHODS: A cross-sectional study was conducted in 2021-2022 among healthcare providers involved in cervical cancer prevention activities in Africa. They were invited to report prior colposcopy training, whether they performed colposcopy and the indications of colposcopy in their practice. RESULTS: Of the 130 providers from 23 African countries who responded to the survey (mean age [SD]: 39.0 years [9.4]), half were female (65 [50.0%]), and 90.7% reported working in urban areas. Overall, only 12.6% of respondents indicated having received prior training on colposcopy, and 11.7% reported that they were performing colposcopy in their current practice. Among the providers who reported performing colposcopy in their practice, colposcopy was indicated for routine cervical cancer screening in 21.2% of clinicians, to better visualize the transformation zone in 15.2% of respondents, to further assess the vascularization of cervical mucosa in 33.3% of respondents, and to determine the appropriate treatment modality in 12.1% of respondents. Providers who performed colposcopy in their practice reported a median number of 30 (interquartile range: 19-65) colposcopic procedures in the past 6 months. CONCLUSION: Providers' training and practice of colposcopy for cervical cancer screening remain suboptimal in Africa. To increase utilization of colposcopy in the region, further training is needed to improve providers' knowledge and engagement. With the development of lower-cost and portable colposcopes, efforts to equip cervical cancer prevention programs and facilities with colposcopy should be enhanced to ensure that women can be screened and managed appropriately in the clinical setting and communities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".