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Record W3154955381 · doi:10.1186/s13054-021-03561-1

Validation of the Critical-Care Pain Observation Tool-Neuro in brain-injured adults in the intensive care unit: a prospective cohort study

2021· article· en· W3154955381 on OpenAlexafffundabout
Céline Gélinas, Mélanie Berube, Kathleen Puntillo, Mădălina Boitor, Mélissa Richard-Lalonde, Françis Bernard, Virginie Williams, Aaron M. Joffe, Craig Steiner, Rebekah Marsh, Louise Rose, Craig Dale, Darina M. Tsoller, Manon Choinière, David L. Streiner

Bibliographic record

VenueCritical Care · 2021
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsJewish General HospitalSunnybrook Health Science CentreMcGill UniversityHôpital du Sacré-Cœur de MontréalMcMaster UniversityThe Quebec Population Health Research NetworkUniversité du Québec à MontréalUniversity of TorontoSt. Joseph’s Healthcare HamiltonCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de MontréalUniversité LavalCentre Hospitalier de l’Université de Montréal
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchRéseau de recherche portant sur les interventions en sciences infirmières du Québec
KeywordsMedicineGlasgow Coma ScaleIntensive care unitDeliriumIntensive careTraumatic brain injuryProspective cohort studyMinimally conscious statePain scalePain assessmentPhysical therapyAnesthesiaEmergency medicineIntensive care medicineInternal medicinePain managementConsciousnessPsychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Pain assessment in brain-injured patients in the intensive care unit (ICU) is challenging and existing scales may not be representative of behavioral reactions expressed by this specific group. This study aimed to validate the French-Canadian and English revised versions of the Critical-Care Pain Observation Tool (CPOT-Neuro) for brain-injured ICU patients. METHODS: A prospective cohort study was conducted in three Canadian and one American sites. Patients with a traumatic or a non-traumatic brain injury were assessed with the CPOT-Neuro by trained raters (i.e., research staff and ICU nurses) before, during, and after nociceptive procedures (i.e., turning and other) and non-nociceptive procedures (i.e., non-invasive blood pressure, soft touch). Patients who were conscious and delirium-free were asked to provide their self-report of pain intensity (0-10). A first data set was completed for all participants (n = 226), and a second data set (n = 87) was obtained when a change in the level of consciousness (LOC) was observed after study enrollment. Three LOC groups were included: (a) unconscious (Glasgow Coma Scale or GCS 4-8); (b) altered LOC (GCS 9-12); and (c) conscious (GCS 13-15). RESULTS: Higher CPOT-Neuro scores were found during nociceptive procedures compared to rest and non-nociceptive procedures in both data sets (p < 0.001). CPOT-Neuro scores were not different across LOC groups. Moderate correlations between CPOT-Neuro and self-reported pain intensity scores were found at rest and during nociceptive procedures (Spearman rho > 0.40 and > 0.60, respectively). CPOT-Neuro cut-off scores ≥ 2 and ≥ 3 were found to adequately classify mild to severe self-reported pain ≥ 1 and moderate to severe self-reported pain ≥ 5, respectively. Interrater reliability of raters' CPOT-Neuro scores was supported with intraclass correlation coefficients > 0.69. CONCLUSIONS: The CPOT-Neuro was found to be valid in this multi-site sample of brain-injured ICU patients at various LOC. Implementation studies are necessary to evaluate the tool's performance in clinical practice.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.167
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.032
GPT teacher head0.338
Teacher spread0.306 · 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

Citations37
Published2021
Admission routes3
Has abstractyes

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