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Record W2171500849 · doi:10.1136/jnnp-2011-301449

Changes in quality of life scores with intravenous immunoglobulin or plasmapheresis in patients with myasthenia gravis

2012· article· en· W2171500849 on OpenAlexaff
Carolina Barnett, Graham M. Wilson, David Barth, Hans Katzberg, Vera Bril

Bibliographic record

VenueJournal of Neurology Neurosurgery & Psychiatry · 2012
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsUniversity Health NetworkToronto General Hospital
Fundersnot available
KeywordsMyasthenia gravisMedicinePlasmapheresisQuality of life (healthcare)Internal medicineAntibodyPhysical therapyPediatricsImmunology

Abstract

fetched live from OpenAlex

BACKGROUND: Intravenous immunoglobulin (IVIG) and plasmapheresis (plasma exchange (PLEX)) have comparable efficacy in reducing the Quantitative Myasthenia Gravis Score for disease severity (QMGS) in patients with moderate to severe myasthenia gravis (MG). OBJECTIVE: To determine if the improvement in the quality of life (QOL) after immunomodulation is comparable with either IVIG or PLEX. METHODS: 62 patients participated in the MG-QOL-60 study, completing the questionnaire at baseline and at day 14 after treatment. The MG-QOL-15 scores were computed from the MG-QOL-60 questionnaire responses. We analysed the change in the QOL scores from baseline to day 14 in both treatment groups. RESULTS: The scores in both QOL scales decreased at day 14 in the IVIG and PLEX groups, without significant difference between groups (QOL-15: IVIG -5.7 ± 8.5, PLEX: -7.0 ± 7.6, p=0.52; QOL-60: IVIG -13.3 ± 16.9, PLEX -18.5 ± 22.0, p = 0.41). The improvement in QOL showed a good correlation with the decrease in QMGS. There was an excellent correlation between the MG-QOL-15 and MG-QOL-60 scores at baseline and at day 14. CONCLUSIONS: This study of MG-QOL changes supports recent findings that IVIG and PLEX are comparable in the treatment of patients with moderate to severe MG and worsening symptoms. Furthermore, our study supports the use of the MG-QOL-15 as a secondary outcome measure in future clinical trials in MG.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.041
Threshold uncertainty score0.844

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.253
Teacher spread0.237 · 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 teacher head, 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

Citations35
Published2012
Admission routes1
Has abstractyes

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