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Enregistrement W4389247022 · doi:10.1182/blood-2023-173878

Novel Multidimensional Pain Assessment Tool Is a Feasible, Valid, and Enjoyable Approach to Communication of Pain Symptoms in Pediatric Sickle Cell Disease

2023· article· en· W4389247022 sur OpenAlexaboutno aff
Erica Mamauag, Charles R. Jonassaint, Jude Jonassaint, Li Ping Wang, Nathan Matten, Cheryl A. Hillery

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueHemoglobinopathies and Related Disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPain assessmentPopulationPhysical therapyMedicineRating scaleDiseasePain scaleVisual analogue scaleCognitionPhysical medicine and rehabilitationPsychologyPain managementDevelopmental psychologyPsychiatryPathology

Résumé

récupéré en direct d'OpenAlex

Background: Pain is the most common symptom of sickle cell disease (SCD) and is especially difficult to assess in the pediatric population. Children have varied developmental stages and cognitive abilities making the communication of this subjective experience hard to operationalize. Both the assessment of pain and validation of these assessments is challenging. No pediatric pain assessment tool has been deemed valid and reliable across all ages and types of pain. Commonly used pediatric tools include the Wong-Baker Faces scale, the numeric rating scale, and the visual analog scale (VAS), which combines the visuals of faces and numeric rating. These are unidimensional - only assessing the severity of pain, and in the case of faces, can cause confusion between pain severity and patient affect. In children with SCD, accurate pain assessment is crucial to current and lifelong management. “Painimation” is a technology-based pain assessment tool that incorporates conceptual animations, rather than numbers or words, to characterize pain. These animations are intended to help clinicians better understand pain from the patient's perspective. They are dynamic and transcend language. For example, animations are designed to communicate dull pulsating pain, sharp electric pain, or anything in between. “Painimation” does not rely on verbal communication skills, making it ideal for use in the pediatric population. “Painimation” has been validated in adults with SCD and is currently in implementation trials. Our study aims to determine the feasibility and validity of “Painimation” in pediatric SCD. Methods: This is a single site non-randomized cross-sectional feasibility and validity study. Participants were 10 - 21 years old with SCD who presented to clinic in baseline health. Participants completed the Painimation application, which consists of a numeric pain rating scale (1-10), a front and back 2-dimensional body image that can be shaded to indicate areas affected by pain, and eight abstract animations intended to represent different pain qualities (tingling, shooting, stabbing, throbbing, pounding, cramping, electrifying, and burning). The patient chose up to three animations and the intensity of each chosen animation was adjusted using a sliding bar without numeric labels. The animations were presented to the patient without labeling the intended quality. The patients then completed a survey consisting of questions regarding the usability of Painimation, clinical questions regarding their disease, Lansky Play-Performance Scale, and validated patient reported outcomes questions (PedsQL, Ped-PRO-CTCAE, PROMIS). Results: We enrolled 30 participants between April 1 and July 31, 2023, with recruitment ongoing. Five records were incomplete and were not analyzed at this time. The mean ratings when asked whether 1) Painimation was easy to use, 2) the patient enjoyed using Painimation, and 3) the patient would use Painimation to communicate pain with their provider were respectively 3.28±.74 (SD), 3.08±.95, and 3.12±1.17 (scale 0-4), indicating a generally favorable result for feasibility. The median VAS score was 5.10 (IQR = 0.25, 8.25). Pain was most reported in the back lower chest (60%), front stomach (48%), and front chest (44%). The stabbing animation was the most chosen at 56% followed by the cramping animation at 20%. Electrifying and Cramping animations had the highest agreement with their intended McGill descriptors at 40%. VAS scores and Painimation severity scores demonstrated excellent positive correlation (Pearson r=.74). Conclusion: Painimation is a feasible pain assessment tool in pediatric SCD with high user satisfaction. Severity scores on Painimation correlate strongly with VAS scores. The pain characteristic conveyed by each animation adds unique dimensions to this measure and will be further analyzed. Painimation is a novel pediatric pain assessment that holds the promise of greatly improving the communication of pain and transforming care in Pediatric SCD.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,015
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,006
Score d'incertitude au seuil0,024

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,015
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,013
Tête enseignante GPT0,255
Écart entre enseignants0,242 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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