P.123 Decoding the brachial plexus : from fundamentals to advances - anatomy, imaging and pathologies
Bibliographic record
Abstract
Background: The brachial plexus provides motor and somatosensory innervation to the upper limb and upper chest. Evaluation of brachial plexus disease is based on history, physical examination etc but imaging plays an important role for lesion localization, characterization and its classification. Effective reporting of imaging findings requires that neuroradiologist should be familiar with the brachial plexus anatomy, relevant landmarks, the spectrum and categories of brachial plexopathies. All above objectives will be discussed in this oral presentation. Methods: Normal brachial plexus anatomy is assumed for five anatomic landmarks: neural foramen, interscalene triangle, lateral border of the first rib, medial border of the coracoid process, and lateral border of the pectoralis minor corresponding to level of roots, trunks, divisions, cords and terminal branches. Conventional radiography has role in evaluating bony injuries. CT has limited role. MR used for comprehensive evaluation of the brachial plexus. Causes of brachial plexopathy are divided into traumatic and nontraumatic with specific features of each. Results: Imaging of brachial plexus is important part of treatment planning and rehabilitation of brachial plexopathies. Confident reporting can be done by knowing basics and injury patterns. Conclusions: Neuroradiologist should have sound knowledge of brachial plexus imaging in order to better contribute to pateint care.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.022 |
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".