Intravenous immunoglobulin response in treatment-nai ve chronic inflammatory demyelinating polyradiculoneuropathy
Bibliographic record
Abstract
OBJECTIVE: There is no consensus on which treatment should be used preferentially in individual patients with chronic inflammatory demyelinating polyneuropathy (CIDP). Patients unlikely to respond to intravenous immunoglobulin (IVIg) could be prescribed corticosteroids first to avoid high cost and a delayed treatment response. We investigated which factors determined a response to IVIg. METHODS: Treatment-naïve patients with CIDP initially treated with at least one full course of IVIg (2 g/kg) at one of two neuromuscular disease centres were included. Patients fulfilled the European Federation of Neurological Societies/Peripheral Nerve Society clinical criteria for CIDP. Significant improvement following IVIg was defined as an improvement (≥ 1 grade) on the modified Rankin scale. Difference in weakness between arms and legs was defined as ≥ 2 grades on the Medical Research Council scale between ankle dorsiflexion and wrist extension. Clinical predictors with a p value <0.15 in univariate analysis were analysed in multivariate logistic regression. RESULTS: Of a total of 281 patients, 214 patients (76%) improved. In univariate analysis, the presence of pain, other autoimmune disease, difference in weakness between arms and legs, and a myelin-associated glycoprotein negative IgM monoclonal gammopathy of undetermined significance were associated with no response to IVIg. In multivariate analysis no pain (p=0.018) and no difference in weakness between arms and legs (p=0.048) were independently associated with IVIg response. Of IVIg non-responders, 66% improved with plasma exchange and 58% with corticosteroids. CONCLUSIONS: IVIg is a very effective first-line treatment. Patients with CIDP presenting with pain or a difference in weakness between arms and legs are less likely to respond to IVIg.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".