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Record W4205762476 · doi:10.1186/s42358-022-00234-7

Guidelines on COVID-19 vaccination in patients with immune-mediated rheumatic diseases: a Brazilian Society of Rheumatology task force

2022· article· en· W4205762476 on OpenAlexaboutno aff
Anna Carolina Faria Moreira Gomes Tavares, Ana Karla Guedes de Melo, Vítor Alves Cruz, Viviane Angelina de Souza, Joana Starling de Carvalho, Ketty Lysie Libardi Lira Machado, Lílian David de Azevedo Valadares, Edgard Torres dos Reis Neto, Rodrigo Poubel Vieira de Rezende, Maria Fernanda Brandão de Resende Guimarães, Gilda Aparecida Ferreira, Alessandra de Sousa Braz, Rejane Maria Rodrigues de Abreu Vieira, Marcelo Antônio Amaro Pinheiro, Sandra Lúcia Euzébio Ribeiro, Blanca Elena Gomes Rios Bica, Kátia Lino, Izaías Pereira da Costa, Cláudia Diniz Lopes Marques, Maria Lúcia Lemos Lopes, José Eduardo Martinez, Rina Dalva Neubarth Giorgi, Lícia Maria Henrique da Mota, Marcos Antônio Araújo da Rocha Loures, Eduardo dos Santos Paiva, Odirlei André Monticielo, Ricardo Machado Xavier, Adriana María Kakehasi, Gecilmara Salviato Pileggi

Bibliographic record

VenueAdvances in Rheumatology · 2022
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsRheumatologyTask forceCoronavirus disease 2019 (COVID-19)MedicineVaccination2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Immune systemImmunologyTask (project management)Internal medicineVirologyPolitical scienceDiseaseManagementOutbreakInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

OBJECTIVE: To provide guidelines on the coronavirus disease 2019 (COVID-19) vaccination in patients with immune-mediated rheumatic diseases (IMRD) to rheumatologists considering specific scenarios of the daily practice based on the shared-making decision (SMD) process. METHODS: A task force was constituted by 24 rheumatologists (panel members), with clinical and research expertise in immunizations and infectious diseases in immunocompromised patients, endorsed by the Brazilian Society of Rheumatology (BSR), to develop guidelines for COVID-19 vaccination in patients with IMRD. A consensus was built through the Delphi method and involved four rounds of anonymous voting, where five options were used to determine the level of agreement (LOA), based on the Likert Scale: (1) strongly disagree; (2) disagree, (3) neither agree nor disagree (neutral); (4) agree; and (5) strongly agree. Nineteen questions were addressed and discussed via teleconference to formulate the answers. In order to identify the relevant data on COVID-19 vaccines, a search with standardized descriptors and synonyms was performed on September 10th, 2021, of the MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, ClinicalTrials.gov, and LILACS to identify studies of interest. We used the Newcastle-Ottawa Scale to assess the quality of nonrandomized studies. RESULTS: All the nineteen questions-answers (Q&A) were approved by the BSR Task Force with more than 80% of panelists voting options 4-agree-and 5-strongly agree-, and a consensus was reached. These Guidelines were focused in SMD on the most appropriate timing for IMRD patients to get vaccinated to reach the adequate covid-19 vaccination response. CONCLUSION: These guidelines were developed by a BSR Task Force with a high LOA among panelists, based on the literature review of published studies and expert opinion for COVID-19 vaccination in IMRD patients. Noteworthy, in the pandemic period, up to the time of the review and the consensus process for this document, high-quality evidence was scarce. Thus, it is not a substitute for clinical judgment.

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)0.079
metaresearch head score (Gemma)0.096
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: Methods · Consensus signal: none
Teacher disagreement score0.079
Threshold uncertainty score0.416

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.096
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0110.007
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0040.005
Research integrity0.0090.007
Insufficient payload (model declined to judge)0.0040.003

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 head0.010
GPT teacher head0.309
Teacher spread0.299 · 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
GenreMethods

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

Citations12
Published2022
Admission routes1
Has abstractyes

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