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Record W2602161709 · doi:10.14740/jnr.v7i1-2.418

Nascent Potential: May It Be a Marker in Prediction of Malignant Course in Acute Motor Axonal Neuropathy?

2017· article· en· W2602161709 on OpenAlexvenueno aff
Halil Önder

Bibliographic record

VenueJournal of Neurology Research · 2017
Typearticle
Languageen
FieldMedicine
TopicPeripheral Neuropathies and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAcute motor axonal neuropathyMedicinePlasmapheresisGuillain-Barre syndromeAxonal degenerationPathophysiologyAntibodyNeurosciencePathologyPediatricsImmunologyPsychology

Abstract

fetched live from OpenAlex

Acute motor axonal neuropathy (AMAN) is a subtype of Guillain-Barre syndrome (GBS) that is associated with a longer duration of illness and worse prognosis. Nonetheless, AMAN recovers well according to acute motor-sensory axonal neuropathy (AMSAN) that has been attributed to the underlying mechanisms such as terminal axonal damage as well as ganglioside antibody related injuries at nodes of Ranvier, rather than a primary axonopathy. On the other hand, in malignant coursed AMAN, ventral root degeneration has been shown to be the responsible mechanism for slow and incomplete recovery in these patients. In this report, I present a patient with malignant coursed AMAN who developed a rapidly, severe course of illness and no significant response to therapies (intravenous immunoglobulin and plasmapheresis) could be achieved even on the second month of follow-up. Via the presentation of this case, I draw attention to nascent potentials on electromyography as a potential paraclinical marker for malignant course in AMAN. Future studies investigating the utilizability of nascent potentials in determination of the prognosis of AMAN may provide vulnerable perspectives. In my opinion, these studies may also add crucial data for clarification of the unsolved pathophysiological mechanisms of AMAN. J Neurol Res. 2017;7(1-2):16-18 doi: https://doi.org/10.14740/jnr418w

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.229
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.067
GPT teacher head0.383
Teacher spread0.316 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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