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microRNAs-based Predictor Factor in Patients with Migraine-ischemic Stroke

2017· review· en· W2594338261 on OpenAlexaff
Luca Gallelli, Antonio Siniscalchi, Marco Carotenuto, Maria Cristina Caroleo, Erika Cione, Vincenzo Guidetti

Bibliographic record

VenueMicroRNA · 2017
Typereview
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMigrainemicroRNAMedicinePathophysiologyStroke (engine)DiseaseBiomarkerIschemic strokeBioinformaticsNeuroscienceInternal medicineIschemiaPsychologyGeneBiologyGenetics

Abstract

fetched live from OpenAlex

Among the clinical spectrum of neurological diseases, migraine is often associated with cerebro-vasculopathy. Impairment of neuroimmune mediators in the central nervous system has been recognized in the pathophysiology of migraine-related stroke. Although genetic correlation was found in patients with migraine-related stroke, the epidemiology of this disease indicates a need in biomarker searching discovery and validation. In this view, small molecule, called microRNAs (miRNAs), able to regulate immune and neuronal processes has been reported in patients with migraine and ischemic stroke and unambiguous miRNAs related to these diseases could be established as new molecular indicator of precocity for clinical and/or pharmacological intervention. Therefore, further exploration of this area is necessary, as greater understanding of these biomarkers could reveal the common mechanisms involved in the pathophysiology of migraine in patients with cerebral infarct.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.621
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.334
Teacher spread0.277 · 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.

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

Citations15
Published2017
Admission routes1
Has abstractyes

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