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Record W2507443246 · doi:10.1097/njh.0000000000000266

Validation of the Critical-Care Pain Observation Tool With Seriously Ill Patients

2016· article· en· W2507443246 on OpenAlexaffabout
Melody Ross, Mădălina Boitor, Céline Gélinas

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsMcGill UniversityJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsInter-rater reliabilityIntraclass correlationDiscriminant validityIntensive care unitMedicineNonverbal communicationCritically illIntensive carePain assessmentCritical illnessPsychologyPhysical therapyClinical psychologyPsychometricsPain managementIntensive care medicineRating scaleDevelopmental psychology

Abstract

fetched live from OpenAlex

The Critical-Care Pain Observation Tool is a behavioral scale recommended for pain assessment in nonverbal critically ill adults. Although the use of this tool was validated in various critical care patient groups, little is known about its validity of use in seriously ill patients at high risk of dying in the intensive care unit, which is the purpose of this study. A prospective cohort study with repeated-measure within-subject design was conducted in a Canadian intensive care unit. Two trained raters assessed the pain of 22 participants with the Critical-Care Pain Observation Tool before, during, and 15 minutes after a nonnociceptive procedure (gentle touch) and a nociceptive procedure (turning). Interrater reliability and discriminant validation were examined. Intraclass correlation coefficients greater than 0.75 between the raters’ scores supported interrater reliability. Higher Critical-Care Pain Observation Tool scores were found during turning compared with gentle touch ( P < 0.01), demonstrating discriminant validation. The Critical-Care Pain Observation Tool seems to be valid, can be used consistently, and is able to discriminate painful from nonpainful conditions in the nonverbal critically ill adults at high risk of dying.

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)0.007
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 score0.063
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.007
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 head0.020
GPT teacher head0.293
Teacher spread0.273 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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