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Record W2093867893 · doi:10.1111/apa.12748

A comparison of three scales for measuring pain in children with cognitive impairment

2014· article· en· W2093867893 on OpenAlexaboutno aff
Marta Massaro, Luca Ronfani, Giovanna Ferrara, Laura Badina, Rita Giorgi, Flavio DʼOsualdo, Andrea Taddio, Egidio Barbi

Bibliographic record

VenueActa Paediatrica · 2014
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCognitionChecklistRating scalePain assessmentPain scalePhysical therapySpearman's rank correlation coefficientCognitive impairmentCorrelationPain managementPsychiatryDevelopmental psychologyPsychologyCognitive psychology

Abstract

fetched live from OpenAlex

AIM: Pain is a neglected problem in children with cognitive impairments, and few studies compare the clinical use of specific pain scales. We compared the Non-Communicating Children's Pain Checklist Postoperative Version (NCCPC-PV), the Echelle Douleur Enfant San Salvador (DESS) and the Children's Hospital of Eastern Ontario Pain Scale (CHEOPS). The first two were developed for children with cognitive impairment, and the third is a more general pain scale. METHODS: Two external observers and the child's caregiver assessed 40 children with cognitive impairment for pain levels. We assessed inter-rater agreement, correlation, dependence on knowledge of the child's behaviour, simplicity and adequacy in pain rating according to the caregiver for all three scales. RESULTS: The correlation between the NCCPC-PV and the DESS was strong (Spearman correlation coefficient = 0.76) and better than between each scale and the CHEOPS. Although the DESS showed better inter-rater agreement, it was more dependent on familiarity with the child and was judged more difficult to use by all observers. The NCCPC-PV was the easiest use and the most appropriate for rating the child's pain. CONCLUSION: The NCCPC-PV was the easiest to use for pain assessment in cognitively impaired children and should be adopted in clinical settings.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.032
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.022
GPT teacher head0.285
Teacher spread0.263 · 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

Citations31
Published2014
Admission routes1
Has abstractyes

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