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Diagnostiquer une neuropathie des petites fibres

2022· article· fr· W4225163181 on OpenAlexaff
Marie Théaudin, François Ochsner, Clovis Adam, Andoni Echaniz‐Laguna, Laurent Magy, Damien Fayolle, Alex Vicino, Annemarie Hübers, Yann Péréon

Bibliographic record

VenueRevue Médicale Suisse · 2022
Typearticle
Languagefr
FieldNeuroscience
TopicHereditary Neurological Disorders
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMedicineEtiologyGynecologySkin biopsyDermatologyBiopsyPathology

Abstract

fetched live from OpenAlex

Small fiber neuropathies affect small, poorly myelinated sensory Aδ and amyelinated C autonomic fibers. Neuropathic pain is often the main symptom. Positive diagnosis is based on the presence of deficient thermo-algesic sensory signs and/or dysautonomic signs with normal neurography. Several tests help to confirm the involvement of small fibers, ranging from simple tests such as the sympathetic skin response to skin biopsy, which measures the density of intraepidermal nerve fibers. The availability of these different tests varies greatly from one center to another. There are multiple etiologies, from rare genetic causes to the more frequent acquired dysimmune or metabolic causes. However, in more than half of the cases, no etiology is identified.

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)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0150.001

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 head0.035
GPT teacher head0.259
Teacher spread0.224 · 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; both teacher heads agree on what is shown here.

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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