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Record W4386244452 · doi:10.17161/rrnmf.v4i3.19553

Challenges managing myasthenia gravis: an international perspective

2023· article· en· W4386244452 on OpenAlexaffabout
Carolina Barnett, Fatmah Alzahmi, Dong Dong, J.T. Heckman, Valeria Salutto, Ha Young Shin

Bibliographic record

VenueRRNMF Neuromuscular Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMyasthenia gravisMedicinePerspective (graphical)Work (physics)Psychological interventionIntensive care medicineEngineeringComputer sciencePsychiatryArtificial intelligence

Abstract

fetched live from OpenAlex

There have been increasing breakthroughs in the diagnosis and treatment of myasthenia gravis over the past decades. However, most published research in myasthenia is conducted in developed regions, such as the US, Canada and Europe. The challenges faced in these regions may be different from other areas of the world, often with fewer resources, such as fewer neurologists, limited or no access to specialised testing for myasthenia, and limited access to some interventions. During the 14th International Conference for Myasthenia Gravis and Myasthenic Disorders, we organized a panel of neurologists and researchers who work with people living with myasthenia in different world regions. The goal was to stimulate discussion around common challenges as well as those that are specific for given areas. Ultimately, we aimed to develop networks of clinicians caring for people living with myasthenia gravis around the world, to improve patient care. We present a summary of challenges using a case format by region, and a discussion around common threads and potential next steps.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.053
GPT teacher head0.335
Teacher spread0.281 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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