Nerve excitability measured with the TROND protocol in amyotrophic lateral sclerosis: a systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT Objective This meta-analysis assessed the 30+ nerve excitability indices generated by the TROND protocol to identify potential biomarkers for ALS. Methods A comprehensive search was conducted in multiple databases, to identify human studies that tested median motor axons. Forest Plot analyses were performed using a random-effects model to determine the pooled effect (Z-score), heterogeneity (I 2 ), and Cohen’s d for potential biomarker identification. Results Out of 2866 studies, 23 studies met the inclusion criteria, incorporating data from 719 controls and 942 ALS patients. Seven indices emerged as potential biomarkers: TEd 90-100 ms, strength-duration time constant (SDTC), superexcitability, TEd 40-60 ms, resting I/V slope, 50% depolarizing I/V, and subexcitability (ranked by the magnitude of the difference between patients and controls from largest to smallest). In a sensitivity analysis focusing on patients with larger compound muscle action potentials (CMAP), only four indices were potential biomarkers: TEd 10-20 ms, TEd 90-100 ms, superexcitability, and SDTC. Conclusion Among the extensive range of 30+ excitability indices generated by the TROND protocol, we have identified seven indices that effectively differentiate ALS patients from healthy controls. Furthermore, a smaller subset of four indices shows promise as potential biomarkers when the CMAP remains relatively large. However, most studies were considered to be at moderate risk of bias due to case-control designs and absence of sensitivity and specificity calculations, underscoring the need for more prospective diagnostic test-accuracy studies with appropriate disease controls. NEW & NOTEWORTHY This meta-analysis uncovers seven potential axonal excitability biomarkers for lower motor neuron pathology in ALS, shedding light on ion channel dysfunction. The identified dysfunction aligns with the primary pathology – protein homeostasis disruption. These biomarkers could fill a gap to detect pre-symptomatic spread of the disease in the spinal cord and monitor treatments targeting protein homeostasis and limiting spread, towards enhancing patient 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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".