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Record W2172340172 · doi:10.3390/brainsci5040521

Objectifying “Pain” in the Modern Neurosciences: A Historical Account of the Visualization Technologies Used in the Development of an “Algesiogenic Pathology”, 1850 to 2000

2015· review· en· W2172340172 on OpenAlex
Frank W. Stahnisch

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBrain Sciences · 2015
Typereview
Languageen
FieldNeuroscience
TopicAesthetic Perception and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsVisualizationCognitive sciencePathologyPsychologyNeuroscienceMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Particularly with the fundamental works of the Leipzig school of experimental psychophysiology (between the 1850s and 1880s), the modern neurosciences witnessed an increasing interest in attempts to objectify "pain" as a bodily signal and physiological value. This development has led to refined psychological test repertoires and new clinical measurement techniques, which became progressively paired with imaging approaches and sophisticated theories about neuropathological pain etiology. With the advent of electroencephalography since the middle of the 20th century, and through the use of brain stimulation technologies and modern neuroimaging, the chosen scientific route towards an ever more refined "objectification" of pain phenomena took firm root in Western medicine. This article provides a broad overview of landmark events and key imaging technologies, which represent the long developmental path of a field that could be called "algesiogenic pathology."

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.

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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0040.000
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 head0.219
GPT teacher head0.408
Teacher spread0.188 · 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