Subcutaneous Immunoglobulin (IgPRO20) for Maintenance Treatment in Patients with Multifocal Motor Neuropathy (P7.094)
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that IgPRO20 (20[percnt] Hizentra) subcutaneous immunoglobulin (SCIG) is a safe and effective therapy in patients with multifocal motor neuropathy (MMN) who have previously responded to intravenous immunoglobulin (IVIG). BACKGROUND: SCIG has been demonstrated to be effective in patients with primary immune deficiency. Case reports have shown potential for use of SCIG in the maintenance therapy of patients with MMN. DESIGN/METHODS: Patients with MMN were enrolled in an open-label, single center trial from December 2012 to October 2014. Weekly SCIG dose was calculated by dividing the average monthly IVIG dose by four and multiplying by a coefficient factor of 1.53 as per product monograph. Training and administration occurred immediately after last IVIG administration. MRC score, grip strength, Guy’s disability Index, SF-36 quality of life and immunoglobulin levels were evaluated at baseline, 3 and 6 months after study start. Results: Fifteen patients (11 males and 4 females) ages 31-82 and mean duration of disease 2-41 years were enrolled in the study. Eleven patients completed the 24-week program with stable disease parameters, expected adverse events including mild-moderate erythema and high patient satisfaction scores (mean 18.2 out of 20, range 17.5-18.9). Three patients receiving 2 g/kg SCIG monthly experienced a drop in IgG levels and worsening upper extremity strength by month 3 leading to IVIG rescue. One patient responding to SCIG up to month 3 developed intolerable skin erythema, swelling and elevated liver enzymes leading to discontinuation of SCIG. Conclusions: Most patients with MMN are able to tolerate infusion and maintain strength when treated with SCIG. In some patients, increasing weakness associated with drop in IgG levels or intolerable adverse reactions may lead to cessation of therapy and mandates close monitoring in all patients receiving this therapy. Study supported by : CSL Behring
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".