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Diagnosis and Treatment of Chronic Immune-mediated Neuropathies

2006· article· en· W1999880924 on OpenAlexaff
Norman Latov, Kenneth C. Gorson, Thomas H. Brannagan, Roy Freeman, Slobodan Apostolski, Alan R. Berger, Walter G. Bradley, Chiara Briani, Vera Bril, Neil A. Busis, Didier Cros, Marinos C. Dalakas, Peter D. Donofrio, P. James B. Dyck, John D. England, Morris A. Fisher, David N. Herrmann, Daniel L. Menkes, Zarife Sahenk, Howard W. Sander, William J. Triggs, J.M. Vallat

Bibliographic record

VenueJournal of Clinical Neuromuscular Disease · 2006
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineChronic inflammatory demyelinating polyneuropathyClinical trialGamma globulinImmune systemDrugMultifocal motor neuropathyImmunologyIntensive care medicineInternal medicineAntibodyPharmacology

Abstract

fetched live from OpenAlex

The chronic autoimmune neuropathies are a diverse group of disorders, whose diagnosis and classification is based on the clinical presentations and results of ancillary tests. In chronic inflammatory demyelinating polyneuropathy, controlled therapeutic trials demonstrated efficacy for intravenous gamma-globulins, corticosteroids, and plasmaphereis. In multifocal motor neuropathy, intravenous gamma-globulins have been shown to be effective. In the other immune-mediated neuropathies, there are no reported controlled therapeutic trials, but efficacy has been reported for some treatments in non-controlled trials on case studies. Choice of therapy in individual cases is based on reported efficacy, as well as severity, progression, coexisting illness, predisposition to developing complications, and potential drug interactions.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.324
Teacher spread0.295 · 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
GenreReview

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

Citations7
Published2006
Admission routes1
Has abstractyes

Explore more

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