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Enregistrement W4213325950 · doi:10.1111/acem.14466

Hot off the press: Stop fallin’—Geriatric fall prevention in the emergency department

2022· letter· en· W4213325950 sur OpenAlexaff
Kirsty Challen, Lauren M. Westafer, William K. Milne

Notice bibliographique

RevueAcademic Emergency Medicine · 2022
Typeletter
Langueen
DomaineMedicine
ThématiqueEmergency and Acute Care Studies
Établissements canadiensWestern University
Organismes subventionnairesNational Heart, Lung, and Blood Institute
Mots-clésMedicineEmergency departmentPsychological interventionFall preventionGerontologyPoison controlMedical emergencySuicide preventionPsychiatry

Résumé

récupéré en direct d'OpenAlex

Close to three million adults aged 65 and over visit American emergency departments (EDs) annually after a fall.1 Approximately 20% of falls result in injuries and, as a result, falls are the most common cause of traumatic injury resulting in older adults presenting to the ED.2 Among this age group, falls are the leading cause of traumatic mortality in this age group.3-5 The article then also describes a consensus conference and involvement of patient advocates in developing five research priorities for the field. This is a scoping review, which means that its aim is to provide a roadmap of the literature on a particular topic and identify key concepts and gaps in the research base. There is a specific PRISMA tool for quality assessment of scoping reviews9 that we have applied. The process in this paper was clearly described and could be replicated; however, quality of the data was limited by the paucity of agreed upon definitions around this topic (including what constitutes a “fall”). This is clear from the level of disagreement between reviewers about what should be included in the second review—the kappa, a measurement of inter-rater reliability was only 0.12 on a scale of −1 to 1, where −1 represents perfect disagreement and 1 represents perfect agreement. The authors justifiably did not attempt a statistical synthesis of the data because the definitions of so many interventions and outcomes varied widely. This scoping review included 32 studies that addressed fall prevention in the ED: three meta-analyses and 23 RCTs, with a total of 571,071 patients. Studies were from 11 countries, 1999–2019, with follow-up from 1 to 18 months. Interventions included falls risk assessment, physical rehabilitation sessions, preventive education, educational guidelines, follow-up with nurse practitioner or physical therapist, and alert devices. Most used recurrent falls as the outcome although anxiety over falls, functional ability, and QALYs also featured. Of these studies, 17 addressed risk stratification and falls care plans: four meta-analyses and eight RCTs, with a total of at least 17,232 patients. Studies were from nine countries, 2011–2018, with follow-up from 6 to 12 months. Eleven screening instruments were identified with interventions including educational, physical therapy, follow-up calls, discharge planning, and home visits. Most used recurrent falls as the outcome. Historically, EM has not screened for post-ED fall-risk https://onlinelibrary.wiley.com/doi/10.1111/acem.12332 in @AcademicEmerMed. More recently, @MauraKennedyMD demonstrated low screening rates even among @EmergencyDocs #GEDA sites https://linkinghub.elsevier.com/retrieve/pii/S0196-0644(21)00513-8 - 48% affirmative #SGEMHOP response seems high. Maybe the poll should have said “a fall risk strategy that is more than a bracelet or different colored socks.” Then see if it is still 48% of EDs! Agree Dr. Southerland. There's a big difference in asking a fall risk question in triage +/− “fall risk” bracelet and putting the procedures & policies in place to encourage safe mobility and address fall risk factors (medication, home safety, appropriate assistive device use). Just one of many great reasons to have a physical therapist on staff in the ED! Fall assessment and prevention is our jam…(among other things). #emergencyPT #ePT #PTinED #paperinapic by @schmadeline and @kirstychallen. Patients may (or may not) benefit from falls screening and interventions. There is inadequate evidence to support a specific tool or intervention across the board but it is likely that multifactorial interventions are most effective.

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)0,015
score de la tête « metaresearch » (Gemma)0,065
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: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,081

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0150,065
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0030,004
Bibliométrie0,0050,004
Études des sciences et des technologies0,0010,001
Communication savante0,0040,005
Science ouverte0,0020,002
Intégrité de la recherche0,0040,003
Charge utile insuffisante (le modèle a refusé de juger)0,0120,002

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 Opus0,054
Tête enseignante GPT0,348
Écart entre enseignants0,294 · 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

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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