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Record W4307164680 · doi:10.4088/pcc.ar21018ah3c

Like a Game of Chess, Every Move Matters

2022· article· en· W4307164680 on OpenAlexaff
Vera Bril, Nicholas J. Silvestri, Carolina Barnett‐Tapia

Bibliographic record

VenueThe Primary Care Companion For CNS Disorders · 2022
Typearticle
Languageen
FieldMedicine
TopicMyasthenia Gravis and Thymoma
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersArgenx
KeywordsTolerabilityMyasthenia gravisAsymptomaticDiseaseMedicineIntensive care medicinePsychologyImmunologySurgeryInternal medicineAdverse effect

Abstract

fetched live from OpenAlex

Symptoms and disease pathophysiology of myasthenia gravis (MG) vary considerably with each patient, and their individual preferences and priorities add to the need for individualized treatment of this autoimmune disease. Research in MG has grown substantially in recent years. New treatments have the potential of being both effective and well tolerated, addressing the trade-off of choosing either efficacy or tolerability when selecting treatments. Promising investigational treatments that may become available in the future may allow more patients than ever before to achieve an asymptomatic state, with the ultimate goal being to turn off abnormal antibody production.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.498

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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 designNot applicable
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

Citations0
Published2022
Admission routes1
Has abstractyes

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