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Record W163619944 · doi:10.1155/2009/542964

Assessing Pain by Facial Expression: Facial Expression as Nexus

2009· review· en· W163619944 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePain Research and Management · 2009
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of Northern British Columbia
FundersCanadian Institutes of Health Research
KeywordsFacial expressionNexus (standard)Expression (computer science)Context (archaeology)Articulation (sociology)Facial Action Coding SystemFacial painPsychologyCognitive psychologyComputer scienceMedicineCommunicationSurgery

Abstract

fetched live from OpenAlex

The experience of pain is often represented by changes in facial expression. Evidence of pain that is available from facial expression has been the subject of considerable scientific investigation. The present paper reviews the history of pain assessment via facial expression in the context of a model of pain expression as a nexus connecting internal experience with social influence. Evidence about the structure of facial expressions of pain across the lifespan is reviewed. Applications of facial assessment in the study of adult and pediatric pain are also reviewed, focusing on how such techniques facilitate the discovery and articulation of novel phenomena. Emerging applications of facial assessment in clinical settings are also described. Alternative techniques that have the potential to overcome barriers to the application of facial assessment arising out of its resource intensiveness are described and evaluated, including recent work on computer- based automatic assessment.

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)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.651
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.125
GPT teacher head0.459
Teacher spread0.334 · 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