Ultrasonographic Identification of Pacinian Corpuscles in the Hand: A Pilot Study of Technique and Reliability
Bibliographic record
Abstract
INTRODUCTION/AIMS: Pacinian corpuscles are end organs of the sensory nervous system. The size and superficial location of Pacinian corpuscles allows them to be visualized with high-resolution ultrasound. This pilot study sought to assess the reliability of Pacinian corpuscle counting in the hand using ultrasound. METHODS: Two healthy participants underwent ultrasound evaluation by three ultrasonographers using a scanning protocol developed for this study. The ultrasound protocol used anatomic landmarks to identify and trace digital nerves in the palms and the adjacent Pacinian corpuscles. The ultrasonographers used morphologic features to identify and count Pacinian corpuscles at two sites in each hand for each participant using a 10-22 MHz linear-array transducer. The procedure was repeated at 2 time points. Inter-rater and intrarater reliabilities were determined using intraclass correlation coefficients (ICCs). RESULTS: Pacinian corpuscles were identified in the second-third and fourth-fifth intermetacarpal spaces. The ultrasound appearance of Pacinian corpuscles is of a hypoechoic, rounded structure without a hyperechoic rim. A clustered appearance and septate internal structure are distinct features of Pacinian corpuscles, but these features are not always present. The mean number at each site was between 10.71 and 11.71. Inter-rater and intrarater reliability both resulted in ICC values over 0.8, indicating "good" inter-rater and intrarater reliabilities. DISCUSSION: Pacinian corpuscles can be reliably counted in the hand using high-resolution ultrasound. The reliability data from this pilot study may facilitate further ultrasound studies of Pacinian corpuscles, which may be decreased in number or undergo other changes in polyneuropathies.
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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 | 0.023 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".