MétaCan
Menu
Back to cohort
Record W3191621473 · doi:10.1212/wnl.0000000000012617

People With Myasthenia Are Getting Better, but Are They Doing Well?

2021· editorial· en· W3191621473 on OpenAlexaff
Chloe ̈ G. K Atkins, Carolina Barnett

Bibliographic record

VenueNeurology · 2021
Typeeditorial
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsCentre for Global Health ResearchUniversity Health Network
Fundersnot available
KeywordsMyasthenia gravisNatural historyRefractory (planetary science)NeurologyDiseaseMedicinePediatricsCross-sectional studyPhysical therapyPsychologyDemographyPsychiatryInternal medicinePathologyBiology

Abstract

fetched live from OpenAlex

The natural history of myasthenia gravis (MG) has changed dramatically over the past century. Multiple studies have shown a steady decline in the proportion of deaths caused by MG.1 This is due in part to improved treatments but also to improved and earlier diagnosis. Greater capacity to diagnosis MG likely means that there are more people living with the disease than previously.2 Current statistics show that most people with myasthenia get better with treatment, with ≈10% to 15% of patients exhibiting refractory disease.3 However, the fact that most patients do better with treatment does not mean that most patients are doing well. In this edition of Neurology ®, Petersson et al.4 report the findings of a large cross-sectional survey of adults living with myasthenia in Sweden that indicates that half of participants reported dissatisfaction with disease control. This means that individuals do not experience enough symptom suppression to allow them to engage in meaningful activities.

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)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.013
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0060.005
Open science0.0040.001
Research integrity0.0130.020
Insufficient payload (model declined to judge)0.0090.010

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 head0.006
GPT teacher head0.227
Teacher spread0.221 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueNeurologySame topicMyasthenia Gravis and ThymomaFrench-language works237,207