Long Latency Reflexes in Clinical Neurology: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Long latency reflexes (LLRs) are impaired in a wide array of clinical conditions. We aimed to illustrate the clinical applications and recent advances of LLR in various neurological disorders from a systematic review of published literature. METHODS: We reviewed the literature using appropriately chosen MeSH terms on the database platforms of MEDLINE, Web of Sciences, and Google Scholar for all the articles from 1st January 1975 to 2nd February 2021 using the search terms "long loop reflex", "long latency reflex" and "C-reflex". The included articles were analyzed and reported using synthesis without meta-analysis (SWiM) guidelines. RESULTS: Based on our selection criteria, 40 articles were selected for the systematic review. The various diseases included parkinsonian syndromes (11 studies, 217 patients), Huntington's disease (10 studies, 209 patients), myoclonus of varied etiologies (13 studies, 127 patients) including progressive myoclonic epilepsy (5 studies, 63 patients) and multiple sclerosis (6 studies, 200 patients). Patients with parkinsonian syndromes showed large amplitude LLR II response. Enlarged LLR II was also found in myoclonus of various etiologies. LLR II response was delayed or absent in Huntington's disease. Delayed LLR II response was present in multiple sclerosis. Among the other diseases, LLR response varied according to the location of cerebellar lesions while the results were equivocal in patients with essential tremor. CONCLUSIONS: Abnormal LLR is observed in many neurological disorders. However, larger systematic studies are required in many neurological disorders in order to establish its role in diagnosis and management.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".