Neuralgic amyotrophy: An update in evaluation, diagnosis, and treatment approaches
Bibliographic record
Abstract
Neuralgic amyotrophy (NA) is an underrecognized peripheral nerve disorder distinguished by severe pain followed by weakness in the distribution of one or more nerves, most commonly in the upper extremity. While classically felt to carry a favorable prognosis, updates in research have demonstrated that patients frequently endure delay in diagnosis and continue to experience long term pain, paresis, and fatigue even years after the diagnosis is made. A transition in therapeutic approach is recommended and described by this review, which emphasizes the necessity to target compensatory abnormal motor control and fatigue by focusing on motor coordination, energy conservation strategies, and behavioral change, rather than strength training which may worsen the symptoms. The development of structural hourglass-like constrictions (HGCs) on imaging can help confirm the suspected clinical diagnosis, and in association with persistent weakness and limited recovery on electrodiagnostic testing may be considered for surgical consultation. Given the complex nature of management, a multidisciplinary approach is described, which can provide an optimal level of care and support for patients with persistent symptoms from NA and allow more unified guidance of rehabilitation and surgical referrals.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".