Multi-Domain Assessment of Autonomic Function in Spinal Cord Injury Using a Modified Autonomic Reflex Screen
Bibliographic record
Abstract
The aim of this study was to characterize autonomic lesions in participants with spinal cord injury (SCI; n = 10) using an autonomic reflex screen, incorporating sudomotor, cardiovagal, and sympathetic adrenergic tests, as well as hemodynamic responses to head-up tilt (HUT). Hemodynamic responses were compared to healthy controls ( n = 20) and previously published normative cutoffs in order better identify autonomic impairments. Sympathetic skin responses (SSRs), heart rate response to deep breathing (HR DB ), and heart rate and beat-to-beat blood pressure responses to Valsalva maneuver (VM) and HUT were measured. SCI participants demonstrated impairment in at least one domain, with 7 of 10 demonstrating autonomic impairment across all domains. No single test was concordant with orthostatic hypotension on HUT, in all participants. Measures of cardiovagal function, including HR DB (SCI = 7.7 ± 3.8 beats/min vs. controls = 17.6 ± 8.1 beats/min) and Valsalva ratio (SCI = 1.53 ± 0.29 vs. controls = 1.85 ± 0.37), were significantly reduced in SCI participants, compared to controls ( p < 0.05). These findings suggest that an autonomic reflex screen, which includes standardized testing protocol and normative data for comparison, is useful for determining the autonomic domains affected by the neurological injury in SCI. We also demonstrated significant cardiovagal impairment in SCI participants compared to controls, which warrants further investigation to determine whether cardiovagal dysfunction is associated with the negative cardiovascular outcomes, which are known to occur in SCI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".