Bibliographic record
Abstract
Identifying “where is the lesion” is particularly important in the approach to the patient with focal dysfunction where a peripheral localization is suspected. This article outlines a methodical approach to the neuromuscular patient in distinguishing focal neuropathies versus radiculopathies, both which are common presentations to the neurology clinic. This approach begins with evaluation of the sensory examination to determine whether there are irritative or negative sensory signs in a peripheral nerve or dermatomal distribution. This is followed by evaluation of deep tendon reflexes to evaluate if differential hyporeflexia can assist in the two localizations. Finally, identification of weak muscle groups unique to a nerve or myotomal pattern in the proximal and distal extremities can most reliably assist in a precise localization. The article concludes with an application of the described method to the common scenario of distinguishing radial neuropathy versus C7 radiculopathy in the setting of a wrist drop and provides additional examples for self-evaluation and reference. The current approach uses the the basics of the neurological examination in a unique way that can be used by a various clinicians to localize these common neuromuscular processes. The learner can then build on this structured approach when encountering more complex neuromuscular presentations, including localizing less common mononeuropathies or plexopathies.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 0.002 |
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".