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Record W2996129989 · doi:10.4037/aacnacc2019952

A Psychometric Analysis Update of Behavioral Pain Assessment Tools for Noncommunicative, Critically Ill Adults

2019· review· en· W2996129989 on OpenAlexaff
Céline Gélinas, Aaron M. Joffe, Paul M. Szumita, Jean‐François Payen, Mélanie Berube, Shiva Shahiri, Mădălina Boitor, Gérald Chanques, Kathleen Puntillo

Bibliographic record

VenueAACN Advanced Critical Care · 2019
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsMcGill UniversityHôpital de l'Enfant-JésusJewish General Hospital
Fundersnot available
KeywordsReliability (semiconductor)Scale (ratio)Critically illPsychometricsPain assessmentClinical psychologyMEDLINEMedicinePsychologyPain managementPhysical therapyIntensive care medicine

Abstract

fetched live from OpenAlex

This is an updated, comprehensive review of the psychometric properties of behavioral pain assessment tools for use with noncommunicative, critically ill adults. Articles were searched in 5 health databases. A total of 106 articles were analyzed, including 54 recently published papers. Nine behavioral pain assessment tools developed for noncommunicative critically ill adults and 4 tools developed for other non-communicative populations were included. The scale development process, reliability, validity, feasibility, and clinical utility were analyzed using a 0 to 20 scoring system, and quality of evidence was also evaluated. The Behavioral Pain Scale, the Behavioral Pain Scale-Nonintubated, and the Critical-Care Pain Observation Tool remain the tools with the strongest psychometric properties, with validation testing having been conducted in multiple countries and various languages. Other tools may be good alternatives, but additional research on them is necessary.

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)0.000
metaresearch head score (Gemma)0.058
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.957
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.058
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.082
GPT teacher head0.474
Teacher spread0.392 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations71
Published2019
Admission routes1
Has abstractyes

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