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Record W4290727496 · doi:10.1007/s00415-022-11295-5

The late onset of emotional distress in people with progressive multiple sclerosis during the Covid-19 pandemic: longitudinal findings from the CogEx study

2022· article· en· W4290727496 on OpenAlexafffund
Anthony Feinstein, Maria Pia Amato, Giampaolo Brichetto, Jeremy Chataway, Nancy D. Chiaravalloti, Gary Cutter, Ulrik Dalgas, John DeLuca, Rachel Farrell, Peter Feys, Massimo Filippi, Jennifer Freeman, Matilde Inglese, Cecilia Meza, Robert W. Motl, Maria A. Rocca, Brian M. Sandroff, Amber Salter

Bibliographic record

VenueJournal of Neurology · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
FundersMultiple Sclerosis Society of Canada
KeywordsCoronavirus disease 2019 (COVID-19)PandemicNeurologyNeuroradiologyMultiple sclerosis2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)DistressMedicineEmotional distressLongitudinal studyPsychologyPsychiatryVirologyClinical psychologyAnxietyInternal medicinePathologyDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: An earlier follow-up study from the CogEx rehabilitation trial showed little change in symptoms of depression, anxiety and psychological distress during the first COVID-19 lockdown compared to pre-pandemic measurements. Here, we provide a second follow-up set of behavioral data on the CogEx sample. METHODS: This was an ancillary, longitudinal follow-up study in CogEx, a randomized controlled trial of exercise and cognitive rehabilitation in people with progressive MS involving 11 centres in North America and Europe. Only individuals impaired on the Symbol Digit Modalities Test (SDMT) were included. Participants repeated the COVID Impact survey administered approximately a year later and completed self-report measures of depression, anxiety and MS symptoms that had been obtained at the trial baseline and during the first COVID Impact survey. Participants who completed the second COVID Impact follow-up were included. To identify predictors of the participants' ratings of their mental and physical well-being, step-wise linear regression was conducted. RESULTS: Of the 131 participants who completed the first COVID impact survey, 74 participants completed the second follow-up survey (mean age 52 (SD = 6.4) years, 62.2% female, mean disease duration 16.4 (SD = 9.0) years, median EDSS 6.0). Pandemic restrictions prevented data collection from sites in Denmark and England (n = 57). The average time between measurements was 11.4 (SD = 5.56) months. There were no significant differences in age, sex, EDSS, disease course and duration between those who participated in the current follow-up study (n = 74) and the group that could not (n = 57). One participant had COVID in the time between assessments. Participants now took a more negative view of their mental/psychological well-being (p = 0.0001), physical well-being (p = 0.0009) and disease course (p = 0.005) compared to their last assessment. Depression scores increased on the HADS-depression scale (p = 0.01) and now exceeded the clinically significant threshold of ≥ 8.0 for the first time. Anxiety scores on the HADS remained unchanged. Poorer mental well-being was predicted by HADS depression scores (p = 0.012) and a secondary-progressive disease course (p = 0.0004). CONCLUSIONS: A longer follow-up period revealed the later onset of clinically significant depressive symptoms on the HADS and a decline in self-perceptions of mental and physical well-being associated with the COVID-19 pandemic relative to the first follow-up data point. TRIAL REGISTRATION: The trial was registered on September 20th 2018 at www. CLINICALTRIALS: gov having identifier NCT03679468. Registration was performed before recruitment was initiated.

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)0.001
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 score0.019
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.090
GPT teacher head0.330
Teacher spread0.240 · 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

Citations4
Published2022
Admission routes2
Has abstractyes

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