Mobile colposcopy by trained nurses in a cervical cancer screening programme at Battor, Ghana
Bibliographic record
Abstract
Cervical precancer screening programs are difficult to establish in low resource settings partly because of a lack of human resource. Our aiming was to overcome this challenge. We hypothesized that this could be done through task shifting to trained nurses.Descriptive retrospective cross-sectional review.Training was at the Cervical Cancer Prevention and Training Center (CCPTC) and screening was carried out at the clinic and at outreaches / peripheral facilities.All women who reported to the clinic for screening or were recruited during outreaches.All 4 nurses were trained for at least 2weeks (module 1). A total of 904 women were screened by the trained nurses using the EVA system. Quality assurance was ensured.Primary screening and follow-up were carried out by the trained nurses with quality assured through image sharing and meetings with peers and experienced gynaecologists.828 women had primary screening and 76 had follow-up screening. 739 (89.3%) were screened at the clinic and 89 (10.7%) at outreaches/peripheral facilities. Of all screened, 130 (14.5%) had cervical lesions, and 25 (2.8%) were treated, 12 (48.0%) by Loop Electrosurgical Excision Procedure (LEEP) performed by a gynaecologist, 11 (44.0%) with thermal coagulation by trained nurses except one, and 2 (8.0%) with cryotherapy by trained nurses.We demonstrate the utility of a model where nurses trained in basic colposcopy can be used to successfully implement a cervical precancer screening and treatment program in low-resource settings.None indicated.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.003 | 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".