A proposal for cervical screening information systems in developing countries
Bibliographic record
Abstract
The effective and efficient delivery of cervical screening programs requires information for planning, management, delivery and evaluation. Specially designed systems are generally required to meet these needs. In many developing countries, lack of information systems constitutes an important barrier to development of comprehensive screening programs and the effective control of cervical cancer. Our report outlines a framework for creating such systems in developing countries and describes a conceptual model for a cervical screening information system. The proposed system is modular, recognizing that there will be considerable between-region heterogeneity in current status and priorities. The proposed system is centered on modules that would allow for the assembly and computerization of data on Pap tests, since these represent the main screening modality at the present time. Additional modules would process data and create and maintain a screening database (e.g., standardize, edit, link and update modules) and allow for the integration of other types of data, such as cervical histopathology results. An open systems development model is proposed, since it is most compatible with the goals of local stakeholder involvement and capacity-building.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".