Mobile colposcopy by trained nurses in a cervical cancer screening programme at Battor, Ghana
Bibliographic record
Abstract
Objectives: 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. Design: Descriptive retrospective cross-sectional review. Setting: Training was at the Cervical Cancer Prevention and Training Center (CCPTC) and screening was carried out at the clinic and at outreaches / peripheral facilities. Participants: All women who reported to the clinic for screening or were recruited during outreaches. Interventions: 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. Main outcome measures: 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. Results: 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. Conclusion: 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. Funding: None indicated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".