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Record W4307496695 · doi:10.1111/ene.15613

Factors associated with the severity of <scp>COVID</scp>‐19 outcomes in people with neuromuscular diseases: Data from the International Neuromuscular <scp>COVID</scp>‐19 Registry

2022· article· en· W4307496695 on OpenAlexfundno aff
Chiara Pizzamiglio, Robert D. S. Pitceathly, Michael P. Lunn, Stefen Brady, Fabiola De Marchi, Lucía Galán, Jeannine M. Heckmann, Alejandro Horga, Mária Judit Molnár, Acary Sousa Bulle Oliveira, Wladimir Bocca Vieira de Rezende Pinto, Guido Primiano, Ernestina Santos, Benedikt Schoser, Serenella Servidei, Paulo Victor Sgobbi de Souza, Venugopalan Y. Vishnu, Michael G. Hanna, Mazen M. Dimachkie, Pedro Machado

Bibliographic record

VenueEuropean Journal of Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
FundersMedical Research Council CanadaClore Duffield FoundationMedical Research CouncilNational Institute for Health and Care Research
KeywordsMedicineOdds ratioConfidence intervalCoronavirus disease 2019 (COVID-19)Logistic regressionMechanical ventilationModified Rankin ScaleInternal medicineSeverity of illnessPediatricsDisease

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Clinical outcome information on patients with neuromuscular diseases (NMDs) who have been infected with SARS-CoV-2 is limited. The aim of this study was to determine factors associated with the severity of COVID-19 outcomes in people with NMDs. METHODS: Cases of NMD, of any age, and confirmed/presumptive COVID-19, submitted to the International Neuromuscular COVID-19 Registry up to 31 December 2021, were included. A mutually exclusive ordinal COVID-19 severity scale was defined as follows: (1) no hospitalization; (2) hospitalization without oxygenation; (3) hospitalization with ventilation/oxygenation; and (4) death. Multivariable ordinal logistic regression analyses were used to estimate odds ratios (ORs) for severe outcome, adjusting for age, sex, race/ethnicity, NMD, comorbidities, baseline functional status (modified Rankin scale [mRS]), use of immunosuppressive/immunomodulatory medication, and pandemic calendar period. RESULTS: Of 315 patients from 13 countries (mean age 50.3 [±17.7] years, 154 [48.9%] female), 175 (55.5%) were not hospitalized, 27 (8.6%) were hospitalized without supplemental oxygen, 91 (28.9%) were hospitalized with ventilation/supplemental oxygen, and 22 (7%) died. Higher odds of severe COVID-19 outcomes were observed for: age ≥50 years (50-64 years: OR 2.4, 95% confidence interval [CI] 1.33-4.31; >64 years: OR 4.16, 95% CI 2.12-8.15; both vs. <50 years); non-White race/ethnicity (OR 1.81, 95% CI 1.07-3.06; vs. White); mRS moderately severe/severe disability (OR 3.02, 95% CI 1.6-5.69; vs. no/slight/moderate disability); history of respiratory dysfunction (OR 3.16, 95% CI 1.79-5.58); obesity (OR 2.24, 95% CI 1.18-4.25); ≥3 comorbidities (OR 3.2, 95% CI 1.76-5.83; vs. ≤2; if comorbidity count used instead of specific comorbidities); glucocorticoid treatment (OR 2.33, 95% CI 1.14-4.78); and Guillain-Barré syndrome (OR 3.1, 95% CI 1.35-7.13; vs. mitochondrial disease). CONCLUSIONS: Among people with NMDs, there is a differential risk of COVID-19 outcomes according to demographic and clinical characteristics. These findings could be used to develop tailored management strategies and evidence-based recommendations for NMD patients.

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)0.001
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.274
Teacher spread0.241 · 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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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