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Record W3024932151 · doi:10.1097/phm.0000000000001468

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

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

Bibliographic record

VenueAmerican Journal of Physical Medicine & Rehabilitation · 2020
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsMedicineTelerehabilitationStroke (engine)PandemicRehabilitationCoronavirus disease 2019 (COVID-19)DiseasePneumoniaMortality rateDiabetes mellitusTelemedicineEmergency medicinePhysical therapyIntensive care medicineHealth careInternal medicineInfectious disease (medical specialty)

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.043
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0080.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.320
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations62
Published2020
Admission routes2
Has abstractyes

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