The role of high-resolution ultrasonography (HRUS) in detecting peripheral neuropathy in leprosy patients and household contacts: a systematic review
Bibliographic record
Abstract
Objectives To explore the utility of high-resolution ultrasonography (HRUS) in leprosy by assessing changes in peripheral nerves. Methods This study follows the Preferred Reporting Item for Systematic Review and Meta-analysis (PRISMA). Relevant studies up to 1 July 2024 were systematically searched using three databases, and the quality of the included studies was further assessed using the Newcastle-Ottawa Scale. Results The results consistently indicated that leprosy patients exhibited significant nerve thickening across various nerves, particularly the ulnar, median, and peroneal nerves with high sensitivity and specificity. Notably, HRUS also demonstrated its ability to detect subclinical nerve involvement in leprosy patients’ household contacts (HHC), suggesting its potential utility in early screening efforts. Conclusion HRUS emerges as a promising diagnostic tool for leprosy, offering enhanced objectivity and superior sensitivity and specificity compared to current diagnostic tools by detecting nerve involvement. This method is effective not only in diagnosing symptomatic patients but also in identifying nerve abnormalities in HHC and individuals without visible skin manifestations.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".