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Record W2074772406 · doi:10.1227/neu.0b013e318215a3b3

MicroRNA and Diseases of the Nervous System

2011· review· en· W2074772406 on OpenAlexaff
David A. Omahen

Bibliographic record

VenueNeurosurgery · 2011
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsFoothills Medical Centre
Fundersnot available
KeywordsmicroRNAMedicineNervous systemNeuroscienceCentral nervous systemFunction (biology)DiseaseComputational biologyBioinformaticsRegulation of gene expressionGeneBiologyGeneticsPathology

Abstract

fetched live from OpenAlex

The recent discovery of microRNAs (miRNAs) has caused the traditional central dogma of molecular biology to be rewritten. miRNAs are small, noncoding pieces of ribonucleic acid that exert control over gene expression at the posttranscriptional level. They appear to play important roles in the normal development and functioning of the nervous system and in disease states. To understand future diagnostic and therapeutic advances arising from the study of miRNAs, it is crucial that neurosurgeons understand what miRNAs are, how they function, and the role they play in diseases of the nervous system. This article serves as a basic introduction to miRNAs and their ever-expanding role in the neurological sciences.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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
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

Citations9
Published2011
Admission routes1
Has abstractyes

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