Pediatric Case Finding in Madagascar: A Controlled, Prospective Population-based Assessment of Key Informant Productivity and Cost
Bibliographic record
Abstract
Purpose: The Key Informant (KI) case finding method, which trains community members to screen children for eye problems and refer them to eye services, is a common strategy to identify and refer children with blindness and visual impairment. However, studies to date have not determined the benefit and cost of adding KIs to routine outreach activities.Methods: Four eye programs in Madagascar with established outreach camps added KIs to a portion of their camps distributed equally throughout their service region over a one year period. KIs recorded children screened and their attendance at an outreach camp. Outreach personnel used standardized registration forms to gather age, sex, visual acuity, diagnosis and treatment data. Costs were gathered for the KI program and outreach camps.Results: In one year, the 4 eye programs held 138 outreach camps, 43 with KIs. The KI camps were more productive than regular camps seeing an average of 61 and 24 children and 50 and 19 children with an eye problem, for KI and regular camps, respectively. The KI camps also saw more children with moderate or severe visual impairment or blindness with 21 and 8 children (per 10 camps) for KI and regular camps, respectively. A KI camp cost $463 ($642 vs. $179) more than a regular camp and $3 ($8 vs. $11) more per child seen.Conclusion: The KI method significantly increased the number of children attending outreach camps, at all levels of visual impairment and blindness, at a modest increase in costs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".