Comparison of electrodiagnostic findings in acute traumatic versus chronic non‐traumatic ulnar neuropathy at the elbow
Bibliographic record
Abstract
INTRODUCTION/AIMS: A common concept is that traumatic nerve injuries are more likely axonal, and that compressive neuropathies are more likely demyelinating. The purpose of this study was to compare traumatic versus non-traumatic ulnar neuropathy at the elbow (UNE) to look for electrodiagnostic differences between the two groups. METHODS: A retrospective 3 year review of UNE patients at two academic health science centers was conducted. Patients were grouped into acute traumatic UNE versus chronic non-traumatic UNE based on clinical history. Electrodiagnostic measurements were compared between the two groups. RESULTS: There were 50 subjects with acute traumatic UNE and 41 with chronic non-traumatic UNE. Mean age and sex distribution were similar but those with traumatic UNE had a 7 month duration of symptoms, while those with chronic UNE had 29 month duration (p < .001). All electrodiagnostic measurements were similar between the two groups including compound muscle action potential amplitudes, motor conduction velocities, frequency of conduction block, sensory nerve studies, and needle electromyography. DISCUSSION: We did not find a difference between the two groups. One should not make inferences regarding acuity or etiology based on electrodiagnostic features alone.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".