MétaCan
Menu
Back to cohort
Record W1479362124 · doi:10.1177/0883073815604228

Validity of the Child Facial Coding System for the Assessment of Acute Pain in Children With Cerebral Palsy

2015· article· en· W1479362124 on OpenAlexafffund
Kellie L. Hadden, Sandra LeFort, Michelle O’Brien, Peter C. Coyte, Denise N. Guerriere

Bibliographic record

VenueJournal of Child Neurology · 2015
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of TorontoJaneway Children's Health and Rehabilitation CentreMemorial University of Newfoundland
FundersCanadian Institutes of Health ResearchNewfoundland and Labrador Centre for Applied Health Research
KeywordsCerebral palsyDiscriminant validityCoding (social sciences)PsychologyRating scaleFace validityPalsyPhysical medicine and rehabilitationMedicinePhysical therapyDevelopmental psychologyPsychometrics

Abstract

fetched live from OpenAlex

The purpose of the current study was to examine the concurrent and discriminant validity of the Child Facial Coding System for children with cerebral palsy. Eighty-five children (mean = 8.35 years, SD = 4.72 years) were videotaped during a passive joint stretch with their physiotherapist and during 3 time segments: baseline, passive joint stretch, and recovery. Children's pain responses were rated from videotape using the Numerical Rating Scale and Child Facial Coding System. Results indicated that Child Facial Coding System scores during the passive joint stretch significantly correlated with Numerical Rating Scale scores (r = .72, P < .01). Child Facial Coding System scores were also significantly higher during the passive joint stretch than the baseline and recovery segments (P < .001). Facial activity was not significantly correlated with the developmental measures. These findings suggest that the Child Facial Coding System is a valid method of identifying pain in children with cerebral palsy.

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)0.003
metaresearch head score (Gemma)0.020
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.281
Teacher spread0.258 · 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

Citations12
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Child NeurologySame topicCerebral Palsy and Movement DisordersFrench-language works237,207