Electrophysiological Responsiveness to Long-Term Therapy in Chronic Inflammatory Demyelinating Polyneuropathy: Case Report
Bibliographic record
Abstract
Electrophysiological studies are essential for the diagnosis of chronic inflammatory demyelinating polyneuropathy (CIDP), but the utility of nerve conduction studies in monitoring outcomes in individual CIDP patients is controversial. Electrophysiological improvements after short-term treatment have been described in large cohorts of CIDP patients, but the magnitude of the changes is small and might be obscured in individual patients due to the variation inherent in nerve conduction testing. We present the case of a CIDP patient treated successfully with immunosuppression and followed for 31 years with serial standardized clinical and electrophysiological evaluations. Improvement in electrophysiological parameters lagged clinical changes for up to 2 years, but all motor parameters improved (distal motor and F wave latencies, conduction velocities, and compound muscle action potential amplitudes) even with evidence of initial axonal damage. Worsening of electrophysiological parameters, specifically increasing F wave latencies, heralded clinical relapse by as much as a year. Electrophysiological parameters do improve with treatment in CIDP patients, although the changes can take up to 2 years, and also worsening electrophysiological parameters can herald clinical relapse and might help guide therapeutic decisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".