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A global systematic analysis of the occurrence, severity, and recovery pattern of long COVID in 2020 and 2021
Sarah Wulf Hanson, Cristiana Abbafati, Joachim G.J.V. Aerts, Ziyad Al‐Aly, Charlie Ashbaugh, Tala Ballouz, Oleg Blyuss, Polina Bobkova, Gouke J. Bonsel, С. Н. Борзакова, Danilo Buonsenso, Denis Butnaru, Austin Carter, Helen Y. Chu, Cristina De Rose, Mohamed Mustafa Diab, Emil Ekbom, Maha El Tantawi, Victor Fomin, Robert Frithiof, Aysylu Gamirova, Petr Glybochko, Juanita A. Haagsma, Shaghayegh Haghjooy Javanmard, Erin B Hamilton, Gabrielle Harris, Majanka H. Heijenbrok‐Kal, Raimund Helbok, Merel E. Hellemons, David Hillus, Susanne M. Huijts, Michael Hultström, Waasila Jassat, Florian Kurth, Ing‐Marie Larsson, Miklós Lipcsey, Chelsea Liu, Callan Loflin, Andreï Malinovschi, Wenhui Mao, Lyudmila Mazankova, Denise J. McCulloch, Dominik Menges, Noushin Mohammadifard, Daniel Munblit, Nikita Nekliudov, Osondu Ogbuoji, И. М. Османов, José L. Peñalvo, Maria Skaalum Petersen, Milo A. Puhan, Md Mujibur Rahman, Verena Rass, Nickolas Reinig, Gerard M. Ribbers, Antonia Ricchiuto, Sten Rubertsson, Э. Р. Самитова, Nizal Sarrafzadegan, Anastasia Shikhaleva, Kyle E Simpson, Dario Sinatti, Joan B. Soriano, Ekaterina Spiridonova, Fridolin Steinbeis, Andrey А. Svistunov, Piero Valentini, Brittney van de Water, Rita J. G. van den Berg-Emons, Ewa Wallin, Martin Witzenrath, Yifan Wu, Hanzhang Xu, Thomas Zöller, Christopher Adolph, James Albright, Joanne O Amlag, Aleksandr Y. Aravkin, Bree Bang-Jensen, Catherine Bisignano, Rachel Castellano, Emma Castro, Suman Chakrabarti, James K. Collins, Xiaochen Dai, Farah Daoud, Carolyn Dapper, Amanda Deen, Bruce Bartholow Duncan, Megan Erickson, Samuel B Ewald, Alize J Ferrari, Abraham D Flaxman, Nancy Fullman, Amiran Gamkrelidze, J Giles, Gaorui Guo, Simon I Hay, Jiawei He, Monika Helak, Erin Hulland, Maia Kereselidze, Kris J Krohn, Alice Lazzar-Atwood, Akiaja Lindstrom, Rafael Lozano, Beatrice Magistro, Déborah Carvalho Malta, Johan Månsson, Ana Maria Mantilla Herrera, Ali H. Mokdad, Lorenzo Monasta, Shuhei Nomura, Maja Pasovic, David M. Pigott, Robert C. Reiner, Grace Reinke, Antonio Luiz P Ribeiro, Damian Santomauro, Aleksei Sholokhov, Emma Elizabeth Spurlock, Rebecca Walcott, Ally Walker, Charles Shey Wiysonge, Peng Zheng, Janet Prvu Bettger, Christopher J L Murray, Theo Vos
Bibliographic record
VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsUniversity of TorontoUniversity of British Columbia
FundersGranskingarráðiðSvenska LäkaresällskapetUppsala UniversitetUniversität ZürichEuroQol Research FoundationBill and Melinda Gates FoundationZonMwVelux StiftungNational Institute for Health and Care ResearchI.M. Sechenov First Moscow State Medical UniversityInstitute for Health Metrics and EvaluationVetenskapsrådetBloomberg PhilanthropiesUK Research and InnovationHjärt-LungfondenUZH FoundationRussian Foundation for Basic ResearchSanofi
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)EconometricsStatisticsVirologyEconomicsMedicineMathematicsInternal medicineOutbreak
Abstract
OpenAlex records an abstract for this work, but it could not be fetched just now.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.024
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.125
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.019 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 head0.013
GPT teacher head0.297
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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 designSystematic review
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
Citations86
Published2022
Admission routes1
Has abstractyes