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Record W4220692194 · doi:10.1212/wnl.0000000000200356

Trajectories of Neurologic Recovery 12 Months After Hospitalization for COVID-19

2022· article· en· W4220692194 on OpenAlexaboutno aff
Jennifer Frontera, Dixon Yang, Chaitanya Medicherla, Samuel Baskharoun, Kristie Bauman, Lena Bell, Dhristie Bhagat, Steven A. Bondi, Alexander Chervinsky, Levi Dygert, Benjamin Fuchs, Daniel Gratch, Lisena Hasanaj, Jennifer Horng, Joshua Zhexue Huang, Ruben Jauregui, Yuan Ji, D. Ethan Kahn, Ethan Koch, Jessica Lin, Susan Liu, Anlys Olivera, Jonathan Rosenthal, Thomas E. Snyder, Rebecca Stainman, Daniel Talmasov, Betsy Thomas, Eduard Valdes, Ting Zhou, Yingrong Zhu, Ariane Lewis, Aaron Lord, Kara Melmed, Sharon B. Meropol, Sujata Thawani, Andrea B. Troxel, Shadi Yaghi, Laura J. Balcer, Thomas Wısnıewskı, Steven Galetta

Bibliographic record

VenueNeurology · 2022
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
FundersNational Institute on Aging
KeywordsCoronavirus disease 2019 (COVID-19)Medicine2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BetacoronavirusEmergency medicinePediatricsVirologyInternal medicineOutbreakDisease

Abstract

fetched live from OpenAlex

<h3>Background and Objective</h3> Little is known about trajectories of recovery 12 months after hospitalization for severe COVID-19. <h3>Methods</h3> We conducted a prospective, longitudinal cohort study of patients with and without neurologic complications during index hospitalization for COVID-19 from March 10, 2020, to May 20, 2020. Phone follow-up batteries were performed at 6 and 12 months after COVID-19 onset. The primary 12-month outcome was the modified Rankin Scale (mRS) score comparing patients with or without neurologic complications using multivariable ordinal analysis. Secondary outcomes included activities of daily living (Barthel Index), telephone Montreal Cognitive Assessment (t-MoCA), and Quality of Life in Neurologic Disorders (Neuro-QoL) batteries for anxiety, depression, fatigue, and sleep. Changes in outcome scores from 6 to 12 months were compared using nonparametric paired-samples sign test. <h3>Results</h3> Twelve-month follow-up was completed in 242 patients (median age 65 years, 64% male, 34% intubated during hospitalization) and 174 completed both 6- and 12-month follow-up. At 12 months, 197/227 (87%) had ≥1 abnormal metric: mRS &gt;0 (75%), Barthel Index &lt;100 (64%), t-MoCA ≤18 (50%), high anxiety (7%), depression (4%), fatigue (9%), or poor sleep (10%). Twelve-month mRS scores did not differ significantly among those with (n = 113) or without (n = 129) neurologic complications during hospitalization after adjusting for age, sex, race, pre–COVID-19 mRS, and intubation status (adjusted OR 1.4, 95% CI 0.8–2.5), although those with neurologic complications had higher fatigue scores (T score 47 vs 44; <i>p</i> = 0.037). Significant improvements in outcome trajectories from 6 to 12 months were observed in t-MoCA scores (56% improved, median difference 1 point; <i>p</i> = 0.002) and Neuro-QoL anxiety scores (45% improved; <i>p</i> = 0.003). Nonsignificant improvements occurred in fatigue, sleep, and depression scores in 48%, 48%, and 38% of patients, respectively. Barthel Index and mRS scores remained unchanged between 6 and 12 months in &gt;50% of patients. <h3>Discussion</h3> At 12 months after hospitalization for severe COVID-19, 87% of patients had ongoing abnormalities in functional, cognitive, or Neuro-QoL metrics and abnormal cognition persisted in 50% of patients without a history of dementia/cognitive abnormality. Only fatigue severity differed significantly between patients with or without neurologic complications during index hospitalization. However, significant improvements in cognitive (t-MoCA) and anxiety (Neuro-QoL) scores occurred in 56% and 45% of patients, respectively, between 6 and 12 months. These results may not be generalizable to those with mild or moderate COVID-19.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.278
Teacher spread0.265 · 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 teacher head, 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".

Quick stats

Citations59
Published2022
Admission routes1
Has abstractyes

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