The “Hierarchical” Scratch Collapse Test for Identifying Multilevel Ulnar Nerve Compression
Bibliographic record
Abstract
BACKGROUND: The Scratch Collapse Test (SCT) is used to assist in the clinical evaluation of patients with ulnar nerve compression. The purpose of this study is to introduce the hierarchical SCT as a physical examination tool for identifying multilevel nerve compression in patients with cubital tunnel syndrome. METHODS: A prospective cohort study (2010-2011) was conducted of patients referred with primary cubital tunnel syndrome. Five ulnar nerve compression sites were evaluated with the SCT. Each site generating a positive SCT was sequentially "frozen out" with a topical anesthetic to allow determination of both primary and secondary ulnar nerve entrapment points. The order or "hierarchy" of compression sites was recorded. RESULTS: Twenty-five patients (mean age 49.6 ± 12.3 years; 64 % female) were eligible for inclusion. The primary entrapment point was identified as Osborne's band in 80 % and the cubital tunnel retinaculum in 20 % of patients. Secondary entrapment points were also identified in the following order in all patients: (1) volar antebrachial fascia, (2) Guyon's canal, and (3) arcade of Struthers. CONCLUSION: The SCT is useful in localizing the site of primary compression of the ulnar nerve in patients with cubital tunnel syndrome. It is also sensitive enough to detect secondary compression points when primary sites are sequentially frozen out with a topical anesthetic, termed the hierarchical SCT. The findings of the hierarchical SCT are in keeping with the double crush hypothesis described by Upton and McComas in 1973 and the hypothesis of multilevel nerve compression proposed by Mackinnon and Novak in 1994.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".