Exploring Active Case Detection Approaches for Leprosy Diagnosis in Varied Endemic Settings: A Comprehensive Scoping Review
Bibliographic record
Abstract
(1) Background: The global burden of leprosy is not shared equally; with the majority of cases being diagnosed in Brazil, India, and Indonesia. Understanding the methods of active case detection (ACD) used in high and low endemic regions is vital for the development of future screening programs. (2) Methods: A systematic search of three databases, PubMed, Embase and Web of Science, was conducted for English language papers, published since the year 2000, which discussed the use of active case detection methods for leprosy screening. The paper utilised the Integrated Screening Action Model (I-SAM) as a tool for the analysis of these methods. (3) Results: 23 papers were identified from 11 different countries. The papers identified 6 different methods of active case detection: Household contact/social contact identification; door-to-door case detection; screening questionnaire distribution; rapid village surveys; school-based screening; and prison-based screening. 15 were located in high endemic regions and 8 of these were located in low endemic regions. (4) Conclusions: For selecting the appropriate methods of active case finding, the leprosy endemicity must be taken into consideration. The findings contribute to policy decision making allowing for more successful future leprosy case detection programs to be designed, ultimately reducing the global burden of the disease, and achieving the WHO's aim of zero leprosy.
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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 | 0.036 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.028 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".