Comparing the <scp>NIS</scp> vs. <scp>MRC</scp> and <scp>INCAT</scp> sensory scale through Rasch analyses
Bibliographic record
Abstract
We performed a comparison between Neuropathy Impairment Scale-sensory (NISs) vs. the modified Inflammatory Neuropathy Cause and Treatment sensory scale (mISS), and NIS-motor vs. the Medical Research Council sum score in patients with Guillain-Barré syndrome (GBS), chronic inflammatory demyelinating polyradiculoneuropathy (CIDP), and IgM monoclonal gammopathy of undetermined significance-related polyneuropathy (MGUSP). The ordinal data were subjected to Rasch analyses, creating Rasch-transformed (RT)-intervals for all measures. Comparison between measures was based on validity/reliability with an emphasis on responsiveness (using the patient's level of change related to the individually obtained varying SE for minimum clinically important difference). Eighty stable patients (GBS: 30, CIDP: 30, and MGUSP: 20) were assessed twice (entry: two observers; 2-4 weeks later: one observer), and 137 newly diagnosed or relapsing patients (GBS: 55, CIDP: 59, and IgM-MGUSP: 23) were serially examined with 12 months follow-up. Data modifications were needed to improve model fit for all measures. The sensory and motor scales demonstrated approximately equal and acceptable validity and reliability scores. Responsiveness scores were poor but slightly higher in RT-mISS compared to RT-NISs. Responsiveness was equal for the RT-motor scales, but higher in GBS compared to CIDP; responsiveness was poor in patients with MGUSP, suggesting a longer duration of follow-up in the latter group of patients.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".