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Global and Specific Behavioral Measures of Pain in Children With Cerebral Palsy

2005· article· en· W2070993279 on OpenAlexaff
Kellie L. Hadden, Carl L. von Baeyer

Bibliographic record

VenueClinical Journal of Pain · 2005
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCerebral palsyMedicineChecklistPhysical therapyCognitionRating scalePhysical medicine and rehabilitationEl NiñoAudiologyDevelopmental psychologyPsychologyPediatricsPsychiatry

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this research was to validate global and behavioral observation methods for measuring pain in children with cerebral palsy (CP). MATERIALS AND METHODS: Nineteen children diagnosed with CP (2-21 years of age) and their primary caregivers participated in this study. Children and their caregivers were videotaped in their home before, during, and after a stretching exercise, and tests of cognitive and social development were administered. Children who were able to pass a training task were also asked to rate their experience of pain using a numerical rating scale (self-report NRS), but only 5 children (24%) passed so their self-report scores were not included. Healthcare professionals rated videotaped segments for each of the 3 time periods in a randomized order using an observer NRS and the Non-Communicating Children's Pain Checklist-Postoperative Version (NCCPC-PV). Raters trained in the Child Facial Coding System (CFCS) examined the same videotaped segments. RESULTS: Results showed significantly greater pain behavior (observer NRS, NCCP- PV) during the stretching procedure than during the baseline and recovery segments. There were no significant differences in CFCS scores, across time segments. CONCLUSIONS: These findings support the hypothesis that children with CP express discernible pain behaviors regardless of cognitive or language ability. These results contribute to multidimensional assessment of pain in children with neurologic impairment.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.180
Threshold uncertainty score0.390

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.053
GPT teacher head0.348
Teacher spread0.295 · 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.

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

Citations50
Published2005
Admission routes1
Has abstractyes

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