Measuring success of targeted screening and prevention for hemoglobinopathies
Bibliographic record
Abstract
Targeted screening for hemoglobinopathies is crucial for preventing affected births and mitigating the psychological and financial burdens on families. This study proposes two novel indicators-Screening index and prevention index-to evaluate the effectiveness of hemoglobinopathy prevention programs. The Screening index measures the proportion of at-risk women who are successfully screened and informed, while the Prevention Index assesses the number of women enrolled per birth prevented, reflecting the program's efficiency in reducing affected births. These indicators account for critical factors such as counselor quality, testing accuracy, timeliness, accessibility, and demographic influences, which impact program success. We also address the limitations of traditional measures and emphasize the need for normalized indicators to adjust for variations in carrier rates across different populations. This approach enhances program monitoring, informs resource allocation, and guides improvements in screening and prevention strategies. Through these measures, we aim to provide a clear understanding of program outcomes, highlight areas for improvement, and offer a cost-efficient approach to prevent hemoglobinopathy-related births.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".