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Record W3186673974 · doi:10.1016/s2665-9913(21)00175-2

Immediate effect of the COVID-19 pandemic on patient health, health-care use, and behaviours: results from an international survey of people with rheumatic diseases

2021· article· en· W3186673974 on OpenAlexaff
Jonathan S. Hausmann, Kevin Kennedy, Julia F. Simard, Jeffrey A. Sparks, Tarin T Moni, Carly Harrison, Maggie Larché, Mitchell Levine, Sebastian E. Sattui, Teresa Semalulu, Gary Foster, Salman Surangiwala, Lehana Thabane, Richard Beesley, Karen Durrant, Elsa F Mateus, S. Mingolla, Michal Nudel, Candace A Palmerlee, Dawn P. Richards, David Liew, Catherine Hill, Suleman Bhana, Wendy Costello, Rebecca Grainger, Pedro Machado, Philip C Robinson, Paul Sufka, Zachary S. Wallace, Jinoos Yazdany, Emily Sirotich, Philip C. Robinson, Jean W. Liew, Namrata Singh, Richard A. Howard, Alfred H.J. Kim, Tiffany Westrich‐Robertson, Edmund Tsui, Alí Duarte‐García, Herman Tam, Arundathi Jayatilleke, Maximilian F. Konig, Elizabeth R. Graef, Michael Putman, Reema Syed, Peter Korsten, Upton A. Laura, Adam Kilian, Yu Pei Eugenia Chock, Douglas W. White, Geraldine T. Zamora, Lisa S Traboco, Aarat Patel, Manuel F. Ugarte‐Gil, Milena Gianfrancesco, Isabelle Amigues, Catalina Sánchez-Álvarez, Laura Trupin, Lindsay Jacobsohn, Bimba F. Hoyer, Kavita Makan, Laure Gossec, Chaudhary Priyank, Jan Leipe, Beth Wallace, Sheila T. Angeles‐Han, Ibrahim Almaghlouth, Wysham D. Katherine, Anthony S. Padula, Françis Berenbaum, Erin M. Treemarcki, Rashmi Sinha, Laura B. Lewandowski, Kate Webb, Kristen Young, Inita Buliņa, Sebastián Herrera, Tamar B. Rubinstein, Marc W. Nolan, Elizabeth Ang, Swamy R. Venuturupalli, Maureen Dubreuil, Cecilia Pisoni, Micaela Cosatti, José Luis Michi Campos, Richard Conway, Tiffany M. Peterson, Christele Felix, Laurie Proulx, Akpabio Akpabio, Angus B. Worthing, Lynn Laidlaw, Pankti Reid, Maria I. Danila, Sahar Lotfi‐Emran, Ngo Q. Linh, Arnav Agarwal, Paul Studenic, Erick Adrian Zamora, Saskya Angevare, Andrea Peirce, Emily C. Somers, Laura C. Cappelli, Brittany A. Frankel, Bharat Kumar, Sonia D. Silinsky Krupnikova, Jorge A. Rosario Vega, Jourdan Frankovich, Ruth Fernandez‐Ruiz, Marcela Posada Velasquez, Su‐Ann Yeoh, Maria Giulia Marino, Chrisiaan Scott, Cecilia Rodríguez, Ana I. Martín Mancheño, Philip Seo, Rocío V. Gamboa‐Cárdenas, Víctor R. Pimentel-Quiroz, Cristina Reátegui-Sokolova, Mari Kihara, Chung Mun Alice Lin, Dheera Kattula, Girgis Laila, Loreto Carmona, John Wallace, Monique Gore‐Massy, Laura-Ann Tomasella, Moré A. Kodek

Bibliographic record

VenueThe Lancet Rheumatology · 2021
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsCanadian Arthritis Patient AllianceMcMaster UniversityQueen's UniversityImpact
FundersCilagVasculitis Clinical Research ConsortiumRocheVasculitis FoundationNational Institute of Arthritis and Musculoskeletal and Skin DiseasesBiogenEuropean League Against RheumatismGilead SciencesDepartment of Health and Aged Care, Australian GovernmentSanofiMeso Scale DiagnosticsAbbVieNational Institute for Health and Care ResearchNovartisPfizerAmerican College of Rheumatology Research and Education Foundation
KeywordsPandemicCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakMedicineSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Health careFamily medicineVirologyPolitical scienceDiseaseOutbreakPathologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.043
GPT teacher head0.347
Teacher spread0.304 · 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".

Quick stats

Citations54
Published2021
Admission routes1
Has abstractno

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