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Record W4317780570 · doi:10.1016/j.ajmo.2023.100033

Epidemiological and clinical perspectives of long COVID syndrome

2023· article· en· W4317780570 on OpenAlexaff
Katherine Huerne, Kristian B. Filion, Roland Grad, Pierre Ernst, Andrea S. Gershon, Mark J. Eisenberg

Bibliographic record

VenueAmerican Journal of Medicine Open · 2023
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreMcGill University Health CentreMcGill UniversityUniversity of TorontoJewish General Hospital
Fundersnot available
KeywordsMedicineAnxietyPandemicPublic healthContext (archaeology)Coronavirus disease 2019 (COVID-19)EpidemiologyDepression (economics)Mental healthIntensive care medicinePsychiatryInternal medicineDiseaseNursing

Abstract

fetched live from OpenAlex

Long COVID, or post-acute COVID-19 syndrome, is characterized by multi-organ symptoms lasting 2+ months after initial COVID-19 virus infection. This review presents the current state of evidence for long COVID syndrome, including the global public health context, incidence, prevalence, cardiopulmonary sequelae, physical and mental symptoms, recovery time, prognosis, risk factors, rehospitalization rates, and the impact of vaccination on long COVID outcomes. Results are presented by clinically relevant subgroups. Overall, 10-35% of COVID survivors develop long COVID, with common symptoms including fatigue, dyspnea, chest pain, cough, depression, anxiety, post-traumatic stress disorder, memory loss, and difficulty concentrating. Delineating these issues will be crucial to inform appropriate post-pandemic health policy and protect the health of COVID-19 survivors, including potentially vulnerable or underrepresented groups. Directed to policymakers, health practitioners, and the general public, we provide recommendations and suggest avenues for future research with the larger goal of reducing harms associated with long COVID syndrome.

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)0.007
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
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 head0.060
GPT teacher head0.451
Teacher spread0.390 · 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.

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

Citations93
Published2023
Admission routes1
Has abstractyes

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