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Enregistrement W3024932151 · doi:10.1097/phm.0000000000001468

Usefulness of Telerehabilitation for Stroke Patients During the COVID-19 Pandemic

2020· article· en· W3024932151 sur OpenAlexaffabout
Min Cheol Chang, Mathieu Boudier‐Revéret

Notice bibliographique

RevueAmerican Journal of Physical Medicine & Rehabilitation · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueStroke Rehabilitation and Recovery
Établissements canadiensCentre Hospitalier de l’Université de Montréal
Organismes subventionnairesnon disponible
Mots-clésMedicineTelerehabilitationStroke (engine)PandemicRehabilitationCoronavirus disease 2019 (COVID-19)DiseasePneumoniaMortality rateDiabetes mellitusTelemedicineEmergency medicinePhysical therapyIntensive care medicineHealth careInternal medicineInfectious disease (medical specialty)

Résumé

récupéré en direct d'OpenAlex

To the Editor: Since the first case of the coronavirus disease (COVID-19) was discovered in Wuhan, Hubei Province, China, in December 2019, it has spread worldwide at an unprecedented rate. Although exact mortality rates vary between countries, a range from 2% to 6% has been reported, with much higher mortality rates among the older people (≥60 years old) and those with underlying health conditions.1 Patients with a history of stroke are reported to be 2.5 times more likely to progress to a severe stage of COVID-19.2 Stroke is highly prevalent among the older patients, and many patients with stroke have other underlying comorbidities, such as diabetes, hypertension, and cardiovascular disease. There is thus an even higher likelihood of disease progression to the severe stage, or even death, among stroke patients with COVID-19. As COVID-19 is transmitted via person-to-person contact, stroke patients undergoing outpatient rehabilitation therapy during the COVID-19 pandemic have an increased risk of infection, as contact with other people often cannot be avoided on the way to and from the hospital. As the frequency of contact increases, the probability of becoming infected with COVID-19 also increases. The first 6 mos after a stroke is a crucial period for recovery, and subacute stroke patients with disabilities regularly undergo rehabilitation therapy at a hospital, which means that these patients have a higher risk of COVID-19. Here, we suggest the utilization of telerehabilitation for stroke patients to reduce their risk of infection. Telerehabilitation refers to “providing rehabilitation service using electronic communication technologies.”3 As such, rehabilitation therapy could be implemented remotely without the physician and patient meeting in person. Although there are many rehabilitation therapy methods and programs based on telerehabilitation, they typically involve the medical staff checking the patient’s condition, showing rehabilitation therapy examples to the patient or their guardian, and using photographs or videos to demonstrate how rehabilitation therapy should be performed. Motor, language, and cognitive functions can be assessed by video or by using specially designed programs. Many studies have analyzed the effectiveness of telerehabilitation, with the majority reporting that telerehabilitation is comparable to in-clinic rehabilitation in terms of improving motor, language, and cognitive functions. In 2019, Cramer et al.3 compared the effectiveness of home-based rehabilitation for stroke patients using telemedicine (62 patients) to that of in-clinic rehabilitation (62 patients). A total of 36 therapy sessions (70 mins each) were designed to improve arm motor function. In this study, both therapy groups displayed significant improvements in arm motor function, showing that telerehabilitation was as effective as in-clinic rehabilitation. Furthermore, more than 50% of stroke patients have depression or anxiety.4 Such psychological problems could be exacerbated during the COVID-19 pandemic, because patients are isolated from the wider community. Drug therapy and counseling must be provided to these patients. With telerehabilitation, patients can receive prescriptions for medication and counseling for psychological stabilization without visiting the hospital. The effectiveness of counseling by telemedicine has been demonstrated in many previous studies.5 Such a service could significantly improve the mental health of stroke patients during the COVID-19 pandemic. With telerehabilitation, a physician can also determine whether a patient needs to be tested for COVID-19. If it is determined that there is no need for a COVID-19 test, then unnecessary hospital visits can be avoided. Moreover, for stroke patients with COVID-19 who are asymptomatic or have mild symptoms and are in self-quarantine at home, telerehabilitation could be used to check for changes in symptoms and quickly detect symptom exacerbation to ensure that they receive on-time treatment. To summarize, we examined the beneficial effects that telerehabilitation may have on stroke patients during the COVID-19 pandemic. Although rehabilitation therapy is essential for such patients, becoming infected with COVID-19 could result in severe illness and death. Protecting stroke patients from COVID-19 is therefore extremely important, and we suggest telerehabilitation as a useful approach in the rehabilitation of stroke patients during the COVID-19 pandemic. Min Cheol Chang, MD Department of Physical Medicine and Rehabilitation College of Medicine Yeungnam University Namku, Taegu, Republic of KoreaMathieu Boudier-Revéret, MD Department of Physical Medicine and Rehabilitation Centre Hospitalier de l’Université de Montréal Montreal, Québec, Canada

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)0,002
score de la tête « metaresearch » (Gemma)0,043
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: aucune
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,026

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,043
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0020,001
Intégrité de la recherche0,0080,007
Charge utile insuffisante (le modèle a refusé de juger)0,0080,002

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 Opus0,026
Tête enseignante GPT0,320
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'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

Citations62
Publié2020
Routes d'admission2
Résumé présentoui

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