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Record W2753347535 · doi:10.1097/nan.0000000000000240

High-Dose Subcutaneous Immunoglobulin in Patients With Multifocal Motor Neuropathy

2017· article· en· W2753347535 on OpenAlexaff
Vilija Rasutis, Hans Katzberg, Vera Bril

Bibliographic record

VenueJournal of Infusion Nursing · 2017
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsUniversity Health Network
FundersCSL Behring
KeywordsMultifocal motor neuropathyMedicineMismatch negativityWeaknessDosingPhysical therapyPhysical medicine and rehabilitationSurgeryInternal medicineElectroencephalography

Abstract

fetched live from OpenAlex

Multifocal motor neuropathy (MMN), an immune neuromuscular condition causing progressive weakness, usually responds to immune-mediated treatments, including intravenous immunoglobulin (IVIG). Fifteen patients with MMN receiving IVIG were enrolled in an open-label, single-center trial and switched to 20% subcutaneous immunoglobulin (SCIG) using a smooth transition protocol (ie, changing the therapy without interruption or impact on the intended outcome of the therapy). Patients received individualized training and support based on motivation and ability to learn, follow directions, and maintain compliance. Although some patients required assistance during the training phase, most managed self-infusion and reported satisfaction in managing therapy autonomously. Educating patients with neuropathies to self-infuse high-dose SCIG at home and with flexibility in dosing schedules was successfully demonstrated in this patient group.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.249
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2017
Admission routes1
Has abstractyes

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