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Record W2153898363 · doi:10.1172/jci43498

Pain imaging in health and disease — how far have we come?

2010· review· en· W2153898363 on OpenAlexafffund
Petra Schweinhardt, M. Catherine Bushnell

Bibliographic record

VenueJournal of Clinical Investigation · 2010
Typereview
Languageen
FieldMedicine
TopicPain Mechanisms and Treatments
Canadian institutionsMcGill University
FundersCanadian Institutes of Health ResearchPfizer CanadaMcGill UniversityInternational Association for the Study of PainLouise and Alan Edwards FoundationPfizer
KeywordsChronic painNeuroscienceNeuroimagingMedicineFunctional imagingDiseasePlaceboBrain Structure and FunctionBrain functionPsychologyPhysical medicine and rehabilitationPathologyAlternative medicine

Abstract

fetched live from OpenAlex

Since modern brain imaging of pain began 20 years ago, networks in the brain related to pain processing and those related to different types of pain modulation, including placebo, have been identified. Functional and anatomical connectivity of these circuits has begun to be analyzed. Imaging in patients suggests that chronic pain is associated with altered function and structural abnormalities in pain modulatory circuits. Moreover, biochemical alterations associated with chronic pain are being identified that provide information on cellular correlates as well as potential mechanisms of structural changes. Data from these brain imaging studies reinforce the idea that chronic pain leads to brain changes that could have functional significance.

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)0.007
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.166
GPT teacher head0.461
Teacher spread0.296 · 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 designOther design
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

Citations216
Published2010
Admission routes2
Has abstractyes

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