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Post–COVID-19 Conditions Among Children 90 Days After SARS-CoV-2 Infection

2022· article· en· W4286560128 on OpenAlexaff
Anna Funk, Nathan Kuppermann, Todd A. Florin, Daniel J. Tancredi, Jianling Xie, Kelly Kim, Yaron Finkelstein, Mark I. Neuman, Marina I. Salvadori, Adriana Yock‐Corrales, Kristen Breslin, Lilliam Ambroggio, Pradip P. Chaudhari, Kelly R. Bergmann, Michael Gardiner, Jasmine R. Nebhrajani, Carmen Campos, Fahd A. Ahmad, Laura F. Sartori, Nidhya Navanandan, Nirupama Kannikeswaran, Kerry Caperell, Claudia R. Morris, Santiago Mintegi, Iker Gangoiti, Vikram Sabhaney, Amy C. Plint, Terry P. Klassen, Usha Avva, Nipam Shah, Andrew Dixon, Maren M. Lunoe, Sarah M. Becker, Alexander J. Rogers, Viviana Pavlicich, Stuart R. Dalziel, Daniel C. Payne, Richard Malley, Meredith L Borland, Andrea K. Morrison, Maala Bhatt, Pedro Rino, Isabel Beneyto Ferré, Michelle Eckerle, April Kam, Shu‐Ling Chong, Laura Palumbo, Maria Y. Kwok, Jonathan C. Cherry, Naveen Poonai, Muhammad Waseem, Norma-Jean Simon, Stephen B. Freedman, Jessica Gómez‐Vargas, Bethany Lerman, James Chamberlain, Adebola Owolabi, Camilla Schanche-Perret Gentil, Sofie Ringold, Jocy Perez, Heidi Vander Velden, Tyrus Crawford, Steven Schultz, Kimberly Ross, Kathy Monroe, Karly Stillwell, Jillian Benedetti, Sharon O’Brien, Kyle Pimenta, Amia Andrade, Adam Isacoff, Kendra Sikes, Nina Gold, Kathleen Reichard, Maureen Nemetski, Pavani Avva, Rakesh D. Mistry, Shanon Young, Marlena Cook, V. Gómez Barrena, Sandra Castejón-Ramírez, María T García Castellanos, Emma Patterson, A Tisherman Samuel, Redjana Carciurmaru, Eleanor Fitzpatrick, Megan Bonisch, Bruce Wright, Mithra Sivakumar, Patricia Candelaria, Vincent Cervantes, Shaminy Manoranjithan, Nabeel Khan, Toni Harbour, Usha Sethuraman, Priya Spencer, Neha Gupta, Amira S. Kamboj, Gael Muanamputu, Guillermo Kohn Loncarica, Eugenia Hernández, Ana Dragovetzky, Angelats Carlos Miguel, Sylvia Torres, Joseph J. Zorc, Rebecca Haber, Ren Mee Hiong, Dianna Sri Dewi, Gary Joubert, Kamary Coriolano Dasilva, Julie Ochs, Alberto Arrighini, Camilla Dallavilla, Andrea Kachelmeyer, Daisy Marty Placencia

Bibliographic record

VenueJAMA Network Open · 2022
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsWestern UniversityDalhousie UniversityMcMaster Children's HospitalChildren’s Health Research InstituteUniversity of AlbertaUniversity of ManitobaChildren's Hospital Research Institute of ManitobaIzaak Walton Killam Health CentreStollery Children's HospitalHospital for Sick ChildrenUniversity of British ColumbiaChildren's Hospital of Eastern OntarioUniversity of CalgaryMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)Prospective cohort studyPediatricsCohortCohort study2019-20 coronavirus outbreakInternal medicineOutbreakDiseaseVirologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Importance: Little is known about the risk factors for, and the risk of, developing post-COVID-19 conditions (PCCs) among children. Objectives: To estimate the proportion of SARS-CoV-2-positive children with PCCs 90 days after a positive test result, to compare this proportion with SARS-CoV-2-negative children, and to assess factors associated with PCCs. Design, Setting, and Participants: This prospective cohort study, conducted in 36 emergency departments (EDs) in 8 countries between March 7, 2020, and January 20, 2021, included 1884 SARS-CoV-2-positive children who completed 90-day follow-up; 1686 of these children were frequency matched by hospitalization status, country, and recruitment date with 1701 SARS-CoV-2-negative controls. Exposure: SARS-CoV-2 detected via nucleic acid testing. Main Outcomes and Measures: Post-COVID-19 conditions, defined as any persistent, new, or recurrent health problems reported in the 90-day follow-up survey. Results: Of 8642 enrolled children, 2368 (27.4%) were SARS-CoV-2 positive, among whom 2365 (99.9%) had index ED visit disposition data available; among the 1884 children (79.7%) who completed follow-up, the median age was 3 years (IQR, 0-10 years) and 994 (52.8%) were boys. A total of 110 SARS-CoV-2-positive children (5.8%; 95% CI, 4.8%-7.0%) reported PCCs, including 44 of 447 children (9.8%; 95% CI, 7.4%-13.0%) hospitalized during the acute illness and 66 of 1437 children (4.6%; 95% CI, 3.6%-5.8%) not hospitalized during the acute illness (difference, 5.3%; 95% CI, 2.5%-8.5%). Among SARS-CoV-2-positive children, the most common symptom was fatigue or weakness (21 [1.1%]). Characteristics associated with reporting at least 1 PCC at 90 days included being hospitalized 48 hours or more compared with no hospitalization (adjusted odds ratio [aOR], 2.67 [95% CI, 1.63-4.38]); having 4 or more symptoms reported at the index ED visit compared with 1 to 3 symptoms (4-6 symptoms: aOR, 2.35 [95% CI, 1.28-4.31]; ≥7 symptoms: aOR, 4.59 [95% CI, 2.50-8.44]); and being 14 years of age or older compared with younger than 1 year (aOR, 2.67 [95% CI, 1.43-4.99]). SARS-CoV-2-positive children were more likely to report PCCs at 90 days compared with those who tested negative, both among those who were not hospitalized (55 of 1295 [4.2%; 95% CI, 3.2%-5.5%] vs 35 of 1321 [2.7%; 95% CI, 1.9%-3.7%]; difference, 1.6% [95% CI, 0.2%-3.0%]) and those who were hospitalized (40 of 391 [10.2%; 95% CI, 7.4%-13.7%] vs 19 of 380 [5.0%; 95% CI, 3.0%-7.7%]; difference, 5.2% [95% CI, 1.5%-9.1%]). In addition, SARS-CoV-2 positivity was associated with reporting PCCs 90 days after the index ED visit (aOR, 1.63 [95% CI, 1.14-2.35]), specifically systemic health problems (eg, fatigue, weakness, fever; aOR, 2.44 [95% CI, 1.19-5.00]). Conclusions and Relevance: In this cohort study, SARS-CoV-2 infection was associated with reporting PCCs at 90 days in children. Guidance and follow-up are particularly necessary for hospitalized children who have numerous acute symptoms and are older.

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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.323
Teacher spread0.306 · 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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Citations116
Published2022
Admission routes1
Has abstractyes

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