A revolution in cervical cancer prevention in Ghana
Bibliographic record
Abstract
Though cervical cancer is largely preventable, success depends on sustained screening and treatment of precancer. This is not available in many low resource settings where screening and treatment services are not available due to a lack of government support. Our vision of setting up a comprehensive cervical cancer prevention scheme across Ghana that offers services tailored to fit every patient's needs, and relies on task shifting has been made possible through the setting up of the Cervical Cancer Prevention and Training Centre (CCPTC) to train and equip middle cadre staff (mostly nurses and midwives) to provide crucial cervical precancer screening and treatment services in many areas of the country that have never seen any such screening activities. To achieve this vision, we have learnt to produce crucial context relevant teaching materials and consumables locally, while adapting simple, readily available social media applications to raise crowd funds to support our work, use these apps to support routine work and to create a network of service providers at various service levels that can rely on each other and assure quality. Our vision has been supported by individuals and organizations that believe in it. They have allowed us to determine our growth and success. By sharing the experiences of the CCPTC we hope to encourage others to set up screening centers in low resource settings.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".