Variability in Intravenous Immunoglobulin G Regimens for Autoimmune Neuromuscular Disorders
Bibliographic record
Abstract
AIMS: We reviewed the intravenous immunoglobulin G (IVIG) dispensing records of a specialty pharmacy to characterize the IVIG treatment regimens used for chronic inflammatory demyelinating polyneuropathy (CIDP) and myasthenia gravis (MG) in community practice. METHODS: Anonymized records were selected based on International Classification of Diseases, Ninth Revision (ICD-9) codes and IVIG treatment for > 1 month. Each patient's immunoglobulin G (IgG) dose per infusion (mg/kg/dose) was multiplied by the number of doses per month (30.5 days divided by the dosing interval in days) to yield the total monthly dose (mg/kg/month). Data were analyzed and summarized using descriptive statistics. RESULTS: Forty-six patients (median age, 56.5 years; range, 8-86 years) fulfilled the inclusion criteria. Thirty-one patients with CIDP received IgG at 7- to 92-day intervals (mean [standard deviation (SD)], 28 [16] days). The mean (SD) IgG dose was 75 (60) g/dose, equivalent to 866 (623) mg/kg/dose and 1145 (778) mg/kg/month. Six patients with stable MG received IVIG or subcutaneous IgG at 3.5- to 61-day intervals (28 [20] days) at a mean (SD) IgG dose of 39 (15) g/dose, equivalent to 405 (108) mg/kg/dose and 783 (680) mg/kg/month. Nine patients with MG with acute exacerbations received IgG at 7- to 42-day intervals (22 [12] days) at a mean (SD) dose of 40 (21) g/dose, equivalent to 403 (172) mg/kg/dose and 641 (288) mg/kg/month. One patient with CIDP and 4 patients with MG were treated with weekly subcutaneous IgG injections. CONCLUSION: Although patients with CIDP and MG are treated with mean total monthly IgG doses similar to those approved by the US Food and Drug Administration, the individual doses and intervals vary considerably, suggesting that physicians may be adjusting IgG dosing according to each patient's clinical condition and treatment response. Further study is necessary to determine the criteria used to adjust IgG treatment regimens and whether these adjustments optimize clinical outcomes while limiting overall costs.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".