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“From ear to trunk”—magnetic resonance imaging reveals referral of pain

2018· article· en· W2804297863 on OpenAlexaff
Julia Forstenpointner, Stephan Wolff, Patrick W. Stroman, Olav Jansen, Stefanie Rehm, Ralf Baron, Janne Gierthmühlen

Bibliographic record

VenuePain · 2018
Typearticle
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsQueen's University
Fundersnot available
KeywordsDermatomeFunctional magnetic resonance imagingMagnetic resonance imagingAllodyniaMedicineTrunkHyperalgesiaReferred painPostherpetic neuralgiaSensory systemNeuroscienceAnesthesiaPhysical medicine and rehabilitationNeuropathic painNociceptionPsychologyRadiology

Abstract

fetched live from OpenAlex

Referred and projecting pain can be observed in acute and chronic pain states. We present the case of a 69-year-old female patient with postherpetic neuralgia in dermatome Th2/3 who reported that touching the ipsilateral earlap (dermatome C2) would enhance pain and dynamic mechanical allodynia in the affected Th2/3-dermatome. The aim was to investigate possible underlying mechanisms of this phenomenon using the capsaicin experimental pain sensitization model, quantitative sensory testing, and functional spinal and supraspinal magnetic resonance imaging. The presented study provides evidence that a referral of pain from the ear to the trunk is possible. We discuss whether the observed phenomenon in combination with activation of pain-modulating areas on functional magnetic resonance imaging suggests either (1) a shift of descending pathways from inhibitory towards facilitating mode or (2) a deafferentation-induced reorganization of somatotopic maps, as the ear and the trunk are adjacent areas of the sensory homunculus. The results and a review about projection of pain in head-neck area are provided.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2018
Admission routes1
Has abstractyes

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