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Enregistrement W4379599151 · doi:10.1001/jamanetworkopen.2023.16383

Partnering With Patients, Caregivers, and Clinicians to Determine Research Priorities for Concussion

2023· article· en· W4379599151 sur OpenAlexafffundabout
Martin H. Osmond, Elizabeth Legace, Peter J. Gill, Rhonda Correll, Katherine Cowan, Jennifer Dawson, Randene Duncan, Erin E. Fox, Kanika Gupta, Ash T Kolstad, Lisa Marie Langevin, Colin Macarthur, Rosemary Macklem, Kinga Olszewska, Nick Reed, Roger Zemek, Mark Bayley, Phil Fait, Isabelle Gagnon, Noah D. Silverberg

Notice bibliographique

RevueJAMA Network Open · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueTraumatic Brain Injury Research
Établissements canadiensAlberta Children's HospitalSickKids FoundationUniversity of TorontoUniversity of CalgaryInstitute for Clinical Evaluative SciencesChildren's Hospital of Eastern OntarioHospital for Sick ChildrenUniversity of Ottawa
Organismes subventionnairesInstitute of Human Development, Child and Youth HealthCanadian Institutes of Health ResearchCHEO Research InstituteHealth CanadaOntario Brain InstituteHospital for Sick ChildrenPhysicians' Services Incorporated FoundationOntario Neurotrauma FoundationPublic Health AgencyUniversity of OttawaPublic Health Agency of CanadaUniversity of Calgary
Mots-clésConcussionSurvey researchMedicineGeneral partnershipDelphi methodFamily medicinePsychologyMedical educationPoison controlInjury preventionApplied psychologyMedical emergencyPolitical science

Résumé

récupéré en direct d'OpenAlex

Importance: Identifying research priorities of patients with concussion, their caregivers, and their clinicians is important to ensure future concussion research reflects the needs of those who will benefit from the research. Objective: To prioritize concussion research questions from the perspectives of patients, caregivers, and clinicians. Design, Setting, and Participants: This cross-sectional survey study used the standardized James Lind Alliance priority-setting partnership methods (2 online cross-sectional surveys and 1 virtual consensus workshop using modified Delphi and nominal group techniques). Data were collected between October 1, 2020, and May 26, 2022, from people with lived concussion experience (patients and caregivers) and clinicians who treat concussion throughout Canada. Exposures: The first survey collected unanswered questions about concussion that were compiled into summary questions and checked against research evidence to ensure they were unanswered. A second priority-setting survey generated a short list of questions, and 24 participants attended a final priority-setting workshop to decide on the top 10 research questions. Main Outcomes and Measures: Top 10 concussion research questions. Results: The first survey had 249 respondents (159 [64%] who identified as female; mean [SD] age, 45.1 [16.3] years), including 145 with lived experience and 104 clinicians. A total of 1761 concussion research questions and comments were collected and 1515 (86%) were considered in scope. These were combined into 88 summary questions, of which 5 were considered answered following evidence review, 14 were further combined to form new summary questions, and 10 were removed for being submitted by only 1 or 2 respondents. The 59 unanswered questions were circulated in a second survey, which had 989 respondents (764 [77%] who identified as female; mean [SD] age, 43.0 [4.2] years), including 654 people who identified as having lived experience and 327 who identified as clinicians (excluding 8 who did not record type of participant). This resulted in 17 questions short-listed for the final workshop. The top 10 concussion research questions were decided by consensus at the workshop. The main research question themes focused on early and accurate concussion diagnosis, effective symptom management, and prediction of poor outcomes. Conclusions and Relevance: This priority-setting partnership identified the top 10 patient-oriented research questions in concussion. These questions can be used to provide direction to the concussion research community and help prioritize funding for research that matters most to patients living with concussion and those who care for them.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,330
Score d'incertitude au seuil0,367

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,255
Tête enseignante GPT0,460
Écart entre enseignants0,204 · 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 tête enseignante, 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

Citations26
Publié2023
Routes d'admission3
Résumé présentoui

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