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Symptom Clusters in Adult Patients with Post-Acute COVID-19

2021· article· en· W3157336238 on OpenAlexaboutno aff
Rajan Shah, Y. Vayner, Samantha Lessen, Daniel Tomer, Michelle N. Gong, Seth Congdon, Aluko A. Hope

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOdds ratioAnxietyInternal medicineQuality of life (healthcare)NauseaProspective cohort studyRisk factorDepression (economics)Physical therapyConfidence intervalPsychiatry

Abstract

fetched live from OpenAlex

Background: As more reports emerge that many COVID-19 survivors suffer protracted and lingering symptoms, how to define and better characterize these patients with post-acute COVID-19 is a research challenge. To understand the overall symptom burden in patients with post-acute COVID-19, we explored whether identifiable symptom clusters exist in a cohort of patients with post-acute COVID-19 and then examined whether these clusters are associated with quality of life impairments. Methods: In a prospective observational study of COVID-19 patients at a Covid-19 Recovery (CORE) clinic between June and December 2020 (eligible patients had either a positive COVID-19 PCR or IgG antibody test), we administered a modified revised Edmonton Symptom Assessment survey which assessed whether patients had 13 symptoms over the week prior to presentation to the clinic on a scale of 0 (no symptoms) to 10 (most severe symptoms). We performed exploratory factor analysis to search for clustering of symptoms into factors and examined the relationship between these clusters and quality of life measures. Results: Across 127 adult patients treated at CORE (mean (standard deviation (SD) age 51.8 (14.0);73.2% were women. We found four symptom factors: emotional factor (including depression and anxiety), activity limiting factor (fatigue, sleepiness and shortness of breath), gastrointestinal symptom factor (nausea, appetite and taste changes) and pain factor (pain, neuropathy). The Cronbach's α for the individual factors ranged from 0.64-0.84. The emotional factor was associated with an increased odds of reporting worse emotional (Odds Ratio (95% Confidence Interval (95% CI) 1.9 (1.2-3.3) and worse cognitive health status (OR 4.9 (2.5-9.5);the activity limiting factor was associated with increase odds of worse physical health status (OR 4.5 (2.1-9.7);the gastrointestinal symptom factor was associated with increase odds of worse cognitive health status (OR 2.1 (1.1-4.2). Conclusions: We found four symptom factors in adult patients with post-acute COVID-19. There was strong internal correlation between the factors and the factors were associated with quality of life measures. Routine assessment of post-acute COVID-19 patient's emotional, gastrointestinal, pain and activity limiting symptoms is important as they may be associated with impaired health related quality of life.

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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)0.004
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.006
GPT teacher head0.272
Teacher spread0.266 · 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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Citations0
Published2021
Admission routes1
Has abstractyes

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