Bibliographic record
Abstract
PURPOSE OF REVIEW: The present review focuses on recent developments in diagnosis and treatment of sensory neuropathies. It does not seek to establish a comprehensive classification of sensory neuropathies, nor treatment guidelines per se. RECENT FINDINGS: Diagnostic criteria and guidelines have been developed for distal symmetric polyneuropathies, small fiber sensory neuropathies and sensory neuronopathies. Novel diagnostic tools such as skin biopsies now allow diagnosis of small fiber sensory neuropathies. Genetic testing has defined new subtypes of mitochondrial neuropathies and inherited neuropathies with sensory involvement. Intravenous immunoglobulin and tumor necrosis factor-alpha inhibitors show promise for some dysimmune sensory neuropathies or neuronopathies. Additional options for management of neuropathic pain are emerging. SUMMARY: Diagnostic methods for both acquired and hereditary sensory neuropathies have progressed in recent years, leading to earlier and more specific diagnoses and a better understanding of disease mechanisms. Much progress remains to be made regarding symptomatic and disease-modifying therapy for a range of sensory neuropathies, including those due to diabetes, HIV infection and from dysimmune or hereditary causes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".