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Enregistrement W1822294459 · doi:10.1111/j.1526-4637.2008.00480.x

Computer-Animated Faces Pain Scale: Commentary on Fanciullo et al. (2007)

2008· letter· en· W1822294459 sur OpenAlexaff
Carl L. von Baeyer, Tiina Jaaniste

Notice bibliographique

RevuePain Medicine · 2008
Typeletter
Langueen
DomaineMedicine
ThématiquePediatric Pain Management Techniques
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesnon disponible
Mots-clésFacial expressionScale (ratio)PreferenceFacial painExpression (computer science)PsychophysicsPsychologyCognitive psychologyMedicineComputer sciencePerceptionCommunicationSurgeryNeuroscienceMathematics

Résumé

récupéré en direct d'OpenAlex

The article by Fanciullo et al. [1], entitled “Development of a new computer method to assess children's pain,” raises many important issues regarding measurement and implementation problems associated with various self-report faces scales designed to assess pain in children. The authors introduce a computer-based faces scale that enables children to rate their pain on a continuous scale by adjusting the facial expression (shape of mouth and eyes) to indicate their pain intensity. We agree with the authors' statements concerning the potential advantages of a computer-based, continuous faces scale, namely sensitivity, patient preference, and computer data acquisition and display. Our experience with such a scale developed in 1997 [2–4] largely supports these assertions. The authors emphasized a presumed direct correspondence between pain intensity and the amount of curvature of the mouth and diameter of the eyes. However, in doing so, they seem to have ignored a basic rule of psychophysics. They assumed that physically equal changes (intervals) in facial expression are perceptually equal intervals. If that were the case, then Mona Lisa's smile would be nothing special. Subtle changes in one dimension may be highly meaningful whereas large changes in another dimension may not. For example, a 50% change in the physical curvature of the mouth does not necessarily correspond to a 50% change in the perceived amplitude or meaning of that change. The methods needed to select perceptually equal intervals on any physical dimension are psychophysical methods, as outlined by Hicks et al. [4]. This approach has been well established since the work of Fechner, published in 1860, who demonstrated that variation in mental events could be measured in relation to variation in physical events [5]. Moreover, in the implementation by Fanciullo et al., eye closure is assumed to reflect greater pain. Although eye closure is a typical feature of the pain face in very young infants, children and adults are less likely to fully close their eyes during a painful experience [6]. Thus, the assumption that the degree of eye closure, from fully opened to fully closed, corresponds proportionally with pain intensity, seems problematic. On the other hand, valid facial indices of pain are not used in Fanciullo's scale, namely brow furrow, mouth stretch, and increasing nasolabial furrow [6,7]. Faces pain scales may be improved by inclusion of validated rather than arbitrarily selected facial expressions of pain. The authors provide a cogent critique of the Wong-Baker FACES Pain Rating Scale, but then, in the following sentence, make an unwarranted leap in generalizing this critique to other scales that were derived using entirely different methodology: “This confluence of measurement and implementation problems indicates that the Faces Pain Rating Scale, as well as the closely related picture scales described in the works cited here, possess the properties of an ordinal scale.” In fact the Faces Pain Scale [8] and the Faces Pain Scale—Revised [4], in work cited by Fanciullo et al., were explicitly designed and tested using psychophysical methods to provide at least interval-level measurement. Fanciullo et al. thus gloss over important differences between the available faces scales. In discussion of implementation of computer pain measurement on personal data assistants (PDA, e.g., Palm Pilot) rather than laptop computers, the authors assert “It would be more problematic to display electronic versions of any of the current paper-and-pencil series of faces on such a small screen.” The authors are correct in suggesting that a full linear array of faces cannot be displayed simultaneously on a small PDA screen. An alternative is to show one face at a time, giving the user the option to scroll across faces and select one. A group working in France has implemented such a PDA-based version of the Faces Pain Scale—Revised for use in clinical trials [9]. They showed that a majority of children preferred the PDA over the paper version of the Faces Pain Scale—Revised. Other electronic, self-report, pain intensity assessment tools that have used a continuous rather than categorical response format with children and adolescents include the e-Ouch [10,11] and the Sydney Animated Facial Expression scale (SAFE; [2–4]). The e-Ouch, designed for children 8 years and older, uses a visual analog scale where the individual rating his/her pain is required to slide a marker on a ruler on a PDA screen from “no pain” to “very much pain”. The SAFE scale, like Fanciullo et al.'s Computer Face Scale, utilizes a line-drawn facial expression that the individual rating his/her pain can change in a continuous way using scroll buttons or a stylus. Although the SAFE scale was rated by children as being easy to use, Goodenough et al. [2] did not find the SAFE to confer any significant psychometric advantage over nonelectronic measures such as the Faces Pain Scale or the Coloured Analogue Scale. Do we need a new, improved, computer-based faces pain scale? In our opinion, yes. In a conference session at the 7th International Symposium on Pediatric Pain in 2006 [12], there was discussion of the desirability of creating a new faces scale taking advantage of everything that has been learned about this way of measuring children's pain since the first of these numerous scales [13] were published in the 1980s. The work of Fanciullo et al. is a step in this direction and we hope they will coordinate their efforts with those of the other groups around the world who are working on this topic.

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)0,004
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,038
Score d'incertitude au seuil0,045

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,025
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,003
Communication savante0,0020,004
Science ouverte0,0040,001
Intégrité de la recherche0,0380,034
Charge utile insuffisante (le modèle a refusé de juger)0,0030,004

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 Opus0,019
Tête enseignante GPT0,288
Écart entre enseignants0,269 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations2
Publié2008
Routes d'admission1
Résumé présentoui

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