Electrophysiological predictors of response to subcutaneous immunoglobulin therapy in chronic inflammatory demyelinating polyneuropathy
Bibliographic record
Abstract
OBJECTIVE: To assess axonal function prior to subcutaneous immunoglobulin (SCIG) therapy or placebo in relation to relapse in chronic inflammatory demyelinating polyneuropathy (CIDP) to determine whether axonal damage can predict therapy response. METHODS: Relapse rates in patients from the Polyneuropathy and Treatment with Hizentra (PATH) study, where patients were treated with placebo or SCIG (IgPro20), were analyzed by baseline (post-intravenous immunoglobulin stabilization) axonal damage (≤1 mV peroneal compound muscle action potential) status. RESULTS: In patients with non-axonal damage, relapses were significantly higher with placebo (73.0%) than IgPro20 (0.2 g/kg: 39.1%, 0.4 g/kg: 19.2%). In patients with axonal damage, IgPro20 had no effect on relapse (placebo: 25.0%, IgPro20: 0.2 g/kg: 30.0%, 0.4 g/kg: 19.4%). Patients with axonal damage relapsed significantly less on placebo versus non-axonal damage, but they also demonstrated higher baseline disability. CONCLUSION: Axonal damage may correspond to relapse upon treatment withdrawal; patients with axonal damage relapse less, possibly reflecting poor response to immunoglobulin therapy, while non-axonal damage patients may experience more relapse, perhaps indicating better treatment response. SIGNIFICANCE: In CIDP patients with axonal loss, immunoglobulin therapy may not be as effective. Assessing axonal damage could help guide therapy, with immunoglobulins ideally used before substantial axonal damage arises.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".