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Ontogeny and phylogeny of facial expression of pain

2015· article· en· W2334945891 on OpenAlexaff
Christine T. Chambers, Jeffrey S. Mogil

Bibliographic record

VenuePain · 2015
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Pharmacology and Anesthesia
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsFacial Action Coding SystemFacial expressionPsychologyFacial musclesCognitionNoxious stimulusChronic painDevelopmental psychologyAudiologyMedicineNeuroscienceNociceptionCommunicationInternal medicine

Abstract

fetched live from OpenAlex

1. Background Facial expression of pain offers an important opportunity for better understanding, assessing, and managing pain. Certain facial muscle movements are sensitive and specific predictors of the presence and severity of pain. The facial display of pain has been found to be relatively consistent across human development (from infancy to adulthood),11 and between humans and nonhuman animals (see below). 2. Facial expression of pain in humans Pain assessment in humans typically relies on self-report; facial expression can be used to quantify pain in individuals who are unable to express themselves verbally (eg, infants, young children, those with verbal or cognitive impairments). This approach was made possible by the Facial Action Coding System of Ekman and Friesen,3 which taxonomizes human facial muscle movements into “action units” (AUs). Certain constellations of these AUs reliably correspond to different human emotional states. The corresponding figure identifies the AUs most commonly associated with pain in infants (Figure A)4 and adults (Figure B).10 The study of facial expression of pain in infants, using the Neonatal Facial Coding System (NFCS),4 provided objective evidence at a time when many doubted the ability of infants to perceive pain. Facial expression of pain is largely a spontaneous reflexive reaction to noxious stimuli, but is, to a certain extent, subject to voluntary control; children as young as 8 years of age are capable of manipulating their facial expression of pain.8 3. Facial expression of pain in nonhuman animals A plethora of new measures of spontaneous pain have been recently developed in response to criticism that preclinical pain researchers were over-reliant on withdrawal responses.9 Given the similar nonverbal status of infants and nonhuman animals, facial expression of pain would seem to provide a solution, especially given Darwin‘s2 direct prediction of phylogenetic continuity of facial expression of emotions. Langford et al.7 adapted the human NFCS to the mouse to create the Mouse Grimace Scale, featuring similar AUs to humans plus 2 rodent-specific changes (in whisker and ear position) (Figure C). Grimace scales have subsequently been developed for the rat,12 rabbit,6 horse1 (Figure D), and cat.5 Quantifying pain through facial expression in these species has proven to have high accuracy and reliability, is useful for indicating both procedural and postoperative pain, and for assessing the efficacy of analgesics. The approach is being increasingly adopted in both veterinary research and care. 4. Conclusions The similarity of facial expression of pain in humans and other animals provides evidence for evolutionary psychological accounts of pain communication13 and represents an impressive example of cross-species translation in pain research. There is a movement towards automated computerized measurement of facial expression of pain, which should eliminate some of the time burden currently associated with its use. Clinical pain continues to be undermanaged in both humans and nonhuman animals. We believe that the study and use of facial expression of pain can effectively address both problems.

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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.086
GPT teacher head0.333
Teacher spread0.248 · 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".

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Citations52
Published2015
Admission routes1
Has abstractyes

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