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Record W2766683794 · doi:10.1212/wnl.0000000000004676

Myasthenia Gravis Impairment Index

2017· article· en· W2766683794 on OpenAlexaff
Carolina Barnett, Vera Bril, Moira K. Kapral, Abhaya V. Kulkarni, Aileen M. Davis

Bibliographic record

VenueNeurology · 2017
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoUniversity Health Network
FundersAmerican Brain Foundation
KeywordsMyasthenia gravisPrednisoneMedicineAnalysis of covarianceInternal medicineGastroenterologyStatisticsMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: To study responsiveness and meaningful change of the Myasthenia Gravis Impairment Index (MGII) and its relative efficiency compared to other measures. METHODS: We enrolled 95 patients receiving prednisone, IV immunoglobulin (IVIg), or plasma exchange (PLEX) and 54 controls. Patients were assessed with the MGII and other measures-including the Quantitative Myasthenia Gravis Score, Myasthenia Gravis Composite, and Myasthenia Gravis Activities of Daily Living-at baseline and 3-4 weeks after treatment. Statistical markers of responsiveness included between-groups and within-group differences, and we estimated the relative efficiency of the MGII compared to other measures. Patient-meaningful change was assessed with an anchor-based method, using the patient's impression of change. We determined the minimal detectable change (MDC) and the minimal important difference (MID) at the group and individual level. RESULTS: = 0.07). For the total MGII score, the individual MDC95 was 9.1 and the MID was 5.5 for individuals and 8.1 for groups. Relative efficiency ratios were >1 favoring the MGII. CONCLUSIONS: The MGII demonstrated responsiveness to prednisone, IVIg, and PLEX in patients with myasthenia. There is a differential response in ocular and generalized symptoms to type of therapy. The MGII has higher relative efficiency than comparison measures and is viable for use in clinical trials.

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)0.000
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 score0.082
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.282
Teacher spread0.267 · 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

Citations49
Published2017
Admission routes1
Has abstractyes

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