Epicritic Sensation in Cervical Spinal Cord Injury: Diagnostic Gains Beyond Testing Light Touch
Bibliographic record
Abstract
Abstract Applied as a bedside test of gross dorsal column function, the testing of light touch (LT) sensation is of high clinical value in the diagnosis of human spinal cord injury (SCI). However, the assessment of overall dorsal column deficit by testing only LT may be limited, because the dorsal column pathway conveys several large diameter afferent modalities (e.g., sensation of touch, two-point discrimination, and proprioception). Therefore, the objective of this study was to compare the epicritic sensation assessed by LT, Semmes-Weinstein monofilament (SWM), and electrical perception threshold (EPT) across cervical dermatomes (C3-C8) in individuals with cervical SCI. A multicenter cross-sectional study was performed at 6 months after cervical SCI, applying combined measures of LT, SWM, and EPT, bilaterally over predefined key sensory points (C3-C8). A total of 300 left- and right-sided dermatomes were tested for each outcome measure in 25 participants. The percentage agreement between classifications according to LT and SWM/EPT testing for all dermatomes between C3 and C8 ranged from 95.5% to 36.2%. The degree of agreement showed considerably variable κ coefficients (-0.1≥kw≤0.7) for each dermatome between C3 and C8. The additional measurements of epicritic sensation by SWM and EPT increased sensitivity by detecting and quantifying differences in sensory thresholds above, at, and below the LT level of injury. This is relevant for early clinical trials (phase 1/2), in which disclosing any biological activity of an intervention may be revealed by subtle sensory changes that might gain a clinical relevance.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".