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Enregistrement W2107183351 · doi:10.1080/16501960410015344

Comments from who for the journal of rehabilitation medicine special supplement on ICF core sets

2004· article· en· W2107183351 sur OpenAlexaboutno aff
Neil Burgess, Somnath Chatterji, Nenad Kostanjsek

Notice bibliographique

RevueJournal of Rehabilitation Medicine · 2004
Typearticle
Langueen
DomaineMedicine
ThématiqueMusculoskeletal Disorders and Rehabilitation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCore (optical fiber)RehabilitationPhysical medicine and rehabilitationPhysical therapyMedicineInternational Classification of Functioning, Disability and HealthPsychologyComputer science

Résumé

récupéré en direct d'OpenAlex

   Health indicators have traditionally focused on deaths anddiseases. While mortality data or diagnostic data on morbidityare important in their own right, they do not adequately capturehealth outcomes of individuals or populations. Diagnosis alonedoes not explain what patients can do, what they need, what theirprognosis will be and what the cost of treatment will be. To dealwith such questions, the International Classification of Function-ing, Disability and Health (ICF) (1) was developed to provide acommon framework for health outcome measurement. The ICFenables us to capture information about the functioning ofindividuals. What happens when people get ill? What they canand cannot do due to their health condition? What difference dothe treatments make? To answer such questions in a clinicallyrelevant manner and to compare across individuals, treatmentsor over time we need common definitions, anchor points and aconsensus on the conceptual framework.The concept of measuring functioning, disability or health isnot new. There are hundreds of assessment tools. Mostlyclinicians in different specialities have developed condition-specific assessment tools (e.g. Arthritis Impact MeasurementScale, AIMS 2; Hamilton Rating Scale of Depression, HAMD;McGill Pain Assessment Questionnaire, MPQ; OutcomeMeasures in Rheumatology Clinical Trials, OMERACT). Thereare also some generic measures (SF-36, Nottingham HealthProfile, EuroQol-5D). These measures have proven useful totrack outcomes, but they are neither comprehensive nor do theyfully map to the ICF. The result, well-known and muchcriticized, is “data silos” in which assessment data acquired inone episode of care – emergency, medical, rehabilitative, out-patient, and community clinical care – cannot be carried over toanother episode of care involving a different clinical focus. Tocompare outcome data across diseases and interventions weneed a common framework that will serve as a “Rosetta Stone”.The ICF makes it possible to link together these data acrossconditions or interventions, eliminating the frustrating data siloeffect, and making for more efficient, transparent, and cost-effective healthcare.A classification needs to be exhaustive by its very nature andbecomes very complex for daily use unless it is transformed intopractice-friendly tools. For example, a clinician cannot easilytake the main volume of ICF and consistently apply it to his orher patients. In daily practice, clinicians will need only a fractionof the categories found in the ICF. As a general rule, 20% of thecodes will explain 80% of the variance observed in practice.With this need in mind, WHO has already created a series ofinstruments based on the ICF, like the ICF Checklist and theWHO Disability Assessment Schedule II (WHO DAS II) (2).The ICF Checklist is a practical translation of the ICF forclinical practice (3). Items from the classification were chosenby experts to list the most commonly used domains, and laterfield tested to verify the selection and make additions of missingitems. The ICF Checklist gives a thumbnail sketch of the mainfunctioning of any individual in terms of body functions andstructures, activities and participation, and environmentalfactors. On the other hand, the WHO DAS II is an assessmentinstrument that gives a total score of disability based on theactivities and participation domains of the ICF. Both instrumentswere explicitly designed to be generic assessment tools usable ina wide range of applications aiming for data comparabilityacross conditions and interventions. This feature constitutes theprimary strength and virtue of these two instruments.However, the generic character of the ICF Checklist and theWHO DAS II may be a drawback in specialty settings. Forexample, a clinician dealing with patients with arthritis will needa wider range of categories to identify functions in theneuromusculoskeletal and movement-related area. A speechand language therapist, on the other hand, will require detaileddescription of voice and speech functions and related structures.This is the dilemma: on the one hand we need a “common base”to compare with other health conditions and interventions; onthe other hand we need “variability” to capture the detail todescribe the profile of a unique group. For such specializedclinical settings, “one (generic) size does not fit all” and the“devil is in the detail”.This obvious clinical requirement has been the primarymotivation for WHO in collaboration with the Department ofPhysical Medicine and Rehabilitation and the newly establishedICF Research Branch of the WHO FIC CC (DIMDI), IMBK atthe Ludwig Maximilian University Munich to develop ICF CoreSets (4). The ICF Core Sets have “common” categories that willhelp to address the comparability issue. These commoncategories are comparable to the generic ICF Checklist. TheICF Core Sets have “additional items” that give a more detailedpicture for 12 chosen clinical conditions. The papers presentedin this volume describe in detail the rigorous scientific processby which these 12 condition specific ICF Core Sets havebeen developed. Interestingly, the papers show not only the

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)0,010
score de la tête « metaresearch » (Gemma)0,071
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 enseignants0,238
Score d'incertitude au seuil0,796

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0100,071
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0020,002
Bibliométrie0,0050,004
Études des sciences et des technologies0,0030,002
Communication savante0,0070,006
Science ouverte0,0040,006
Intégrité de la recherche0,0200,009
Charge utile insuffisante (le modèle a refusé de juger)0,2380,157

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 Opus0,023
Tête enseignante GPT0,348
Écart entre enseignants0,325 · 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

Citations167
Publié2004
Routes d'admission1
Résumé présentoui

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