MétaCan
Menu
Back to cohort
Record W4206912166 · doi:10.1371/journal.pmed.1003891

Pandemic health consequences: Grasping the long COVID tail

2022· article· en· W4206912166 on OpenAlexaff
Kieran L. Quinn, Chaim M. Bell

Bibliographic record

VenuePLoS Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsInstitute for Clinical Evaluative SciencesSinai Health SystemUniversity of Toronto
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)BetacoronavirusCoronavirus InfectionsVirologyMEDLINEMedicineBiologyOutbreakInfectious disease (medical specialty)PathologyDisease

Abstract

fetched live from OpenAlex

Emerging evidence suggests that approximately 10% of people who survive Coronavirus Disease 2019 (COVIDAU : PleasenotethatCOVID À 19hasbeendefinedasCoronavirusDisease2019atitsfirst-19) will have lingering symptoms that negatively affect their quality of life, ability to work, and function [1,2].This important group of people with the post-COVID-19 condition may seem small in comparison to the overall number of people with COVID-19 infection [3].However, many patients who survive COVID-19 are likely to have considerable symptom burden, high resource utilization and health service needs, reduced economic productivity, and possibly a shortened life expectancy.The study by Bhaskaran and colleagues published in PLOS Medicine addresses an evolving, poorly studied, and important area of health policy and planning related to the care of patients who survive hospitalization for COVID-19 [4].At face value, the scope of the COVID-19 pandemic is enormous.Within 2 years, nearly 300 million people have been infected with the Severe Acute Respiratory Syndrome Coronavirus 2 (SARSAU : AU : PleasenotethatSARS À CoV À 2hasbeendefinedasSevereAcuteRespiratorySyndro -CoV-2) virus, and more than 5 million people have died from it [5].But, there is also a long tail to this statistical distribution of hardship.Studies report that numerous patients will continue to experience fatigue, shortness of breath, pain, sleep disturbances, anxiety, and depression [6].More serious organ dysfunction such as pulmonary fibrosis, cognitive impairment, myocarditis, and renal failure may also develop [6].Whether these translate into clinical diagnoses of chronic diseases like interstitial lung disease, dementia, heart failure, and chronic kidney disease remains to be seen.Collectively, the prospect for immense suffering among these individuals will undoubtedly have huge and enduring impacts on healthcare systems globally.As the world continues its largest vaccination effort in history and looks to eliminate the impacts of acute COVID-19, we must not forget that a meaningful minority who survive will transition from an acute to chronic disease state.In turn, management strategies and health resource planning must also appropriately transition.As a multisystem disease, the post-COVID-19 condition will require the involvement of multidisciplinary care teams [7]: Who will help to look after these patients?Bhaskaran and colleagues studied over 164,000 hospitalized adults with COVID-19 matched to an "active control" group of adults hospitalized with influenza and to general population controls.They compared the medium-and long-term risks of hospital admission and death across the 3 study groups.The main findings were that people discharged following hospitalization for COVID-19 had a 2-fold higher associated risk for rehospitalization and death than the general population and similar risks compared to those hospitalized for influenza.These outcomes were most pronounced in the first 30 days following discharge yet remained substantially elevated over time.Further, those hospitalized with COVID-19 were more likely

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)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0070.012
Open science0.0010.007
Research integrity0.0050.012
Insufficient payload (model declined to judge)0.0330.006

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 head0.056
GPT teacher head0.347
Teacher spread0.291 · 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 designNot applicable
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

Explore more

Same venuePLoS MedicineSame topicLong-Term Effects of COVID-19French-language works237,207