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Record W4408893216 · doi:10.47276/lr.96.1.2024132

The role of high-resolution ultrasonography (HRUS) in detecting peripheral neuropathy in leprosy patients and household contacts: a systematic review

2025· review· en· W4408893216 on OpenAlexaboutno aff
Marvel Pratama Tjiaman, Mohamad Zaidan, Zidan Fawwaz Ausath, Fitri Octaviana, Aulia Putri Karima, Sri Linuwih Menaldi

Bibliographic record

VenueLeprosy Review · 2025
Typereview
Languageen
FieldMedicine
TopicLeprosy Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLeprosyPeripheral neuropathyUltrasonographyPeripheral nervePeripheralDermatologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.298
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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