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Record W1846211127 · doi:10.4137/mri.s23556

Assessing Nociception by Fmri of the Human Spinal Cord: A Systematic Review

2015· review· en· W1846211127 on OpenAlexafffund
Tiffany A. Kolesar, Kirsten M. Fiest, Stephen D. Smith, Jennifer Kornelsen

Bibliographic record

VenueMagnetic Resonance Insights · 2015
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaHealth Sciences Centre Foundation
KeywordsNociceptionNoxious stimulusSpinal cordMedicineNeuroscienceDorsumPsychologyPhysical medicine and rehabilitationAnatomyInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess the use of fMRI of the spinal cord in measuring noxious stimulation. METHODS: The Scopus, Medline, EMBASE, and Web of Science databases were searched, along with the reference lists of included articles. Two independent reviewers screened abstracts, full-text articles, and extracted data. Original research was included if fMRI of the human spinal cord was used to measure responses to noxious stimulation. RESULTS: Of the 192 abstracts screened, 19 met the search criteria and were divided according to their focus: investigating pain responses (n = 6), methodology (n = 6), spinal cord injury (n = 2), or cognition-pain interactions (n = 5). All but one study appear to have observed activity in ipsilateral and dorsal gray matter regions in response to noxious stimuli, although contralateral or ventral activity was also widely observed. CONCLUSIONS: Although nociception can be investigated using spinal fMRI, establishing reliability, standardizing methodology, and reporting of results will greatly advance this field.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.083
GPT teacher head0.394
Teacher spread0.311 · 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 designSystematic review
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

Citations17
Published2015
Admission routes2
Has abstractyes

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