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Record W2911558177 · doi:10.3899/jrheum.180784

Vaccination Guidelines for Patients with Immune-mediated Disorders Taking Immunosuppressive Therapies: Executive Summary

2019· article· en· W2911558177 on OpenAlexafffundvenueabout
Kim Papp, Boulos Haraoui, Deepali Kumar, John K. Marshall, Robert Bissonnette, Alain Bitton, Brian Bressler, Melinda Gooderham, Vincent Ho, Shahin Jamal, Janet Pope, A. Hillary Steinhart, Donald C. Vinh, John Wade

Bibliographic record

VenueThe Journal of Rheumatology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmunodeficiency and Autoimmune Disorders
Canadian institutionsMcGill University Health Centre
FundersUniversité de MontréalUniversity of TorontoMcGill University Health CentreFaculty of Medicine, University of British ColumbiaQueen's UniversityAmgenMcMaster UniversityMcGill University
KeywordsMedicineVaccinationImmunologyRheumatologyIntensive care medicinePopulationDiseaseImmunizationImmune systemInfectious disease (medical specialty)Internal medicineEnvironmental health

Abstract

fetched live from OpenAlex

The use of immunosuppressive therapies for immune-mediated disease is associated with an elevated risk of infections and related comorbidities. While many infectious diseases can generally be prevented by vaccines, immunization rates in this specific patient population remain suboptimal, due in part to uncertainty about their efficacy or safety under these clinical situations. To address this concern, a multidisciplinary group of Canadian physicians with expertise in dermatology, gastroenterology, infectious diseases, and rheumatology developed evidence-based clinical guidelines on vaccinations featuring 13 statements that are aimed at reducing the risk of preventable infections in individuals exposed to immunosuppressive and immunomodulatory agents.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.240
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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 teacher head, 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

Citations27
Published2019
Admission routes4
Has abstractyes

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