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Global, regional, and national incidence of six major immune-mediated inflammatory diseases : findings from the global burden of disease study 2019
Bibliographic record
VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2023
Typeother
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Imaging and Inversion Techniques
Canadian institutionsnot available
FundersEuropean and Developing Countries Clinical Trials PartnershipNational Research, Development and Innovation OfficeNational Health and Medical Research CouncilUniversity at BuffaloMedical Research CouncilDebre Tabor UniversityUniversity of Health and Allied SciencesMadda Walabu UniversityUniversitas PadjadjaranINCLIVA Instituto de Investigación SanitariaUniversity of ZanjanUniversity of Agriculture, FaisalabadYasuj University of Medical SciencesCentro de Investigación Biomédica en Red de Salud MentalCentre Hospitalier Universitaire VaudoisShahid Beheshti University of Medical SciencesUniversity of GondarUniversità degli Studi del Piemonte OrientaleInstituto de Salud Carlos IIIShiraz UniversityJimma UniversityShahrekord UniversityAhvaz Jundishapur University of Medical SciencesChinese University of Hong KongMinisterio de Ciencia e InnovaciónTaipei Medical UniversityKerman University of Medical SciencesTehran University of Medical Sciences and Health ServicesSultan Qaboos UniversityIslamic Azad University, ShahrekordShiraz University of Medical SciencesDirectorate for Biological SciencesKing Abdulaziz UniversityJordan University of Science and TechnologyIsfahan University of Medical SciencesScience and Technology Development FundUniversity of PretoriaIslamic Azad UniversityFlinders UniversityCairo UniversityUniversité de Versailles Saint-Quentin-en-YvelinesUniversity of CanberraUniversity of TorontoZanjan University of Medical SciencesStockholms Läns LandstingUniversity of New South WalesPublic Health AgencyAmity UniversityKing Faisal UniversityNational Institute for Health and Care ResearchMinistero della SaluteHarvard UniversityAsian Institute of Medicine, Science and TechnologyIndian Council of Medical ResearchCancer Institute NSWSocial Science Research CouncilUniversity of TsukubaUniversità di CataniaSRM Institute of Science and TechnologyBelgian American Educational FoundationLa Trobe UniversityArabian Gulf UniversityManchester Biomedical Research CentreUniversity of Electronic Science and Technology of ChinaMonash UniversitySouth Eastern Sydney Local Health DistrictShaqra UniversityIran University of Medical SciencesHorizon 2020 Framework ProgrammeShahid Sadoughi University of Medical SciencesUniversitatea de Medicină şi Farmacie "Carol Davila" BucureştiAcademy of Scientific Research and TechnologyUniversity College CorkJazan UniversityLung Foundation AustraliaUniversität BaselEast Carolina UniversityShahrekord University of Medical SciencesRafsanjan University of Medical SciencesPublic Health Agency of CanadaAlborz University of Medical SciencesAin Shams UniversityCleveland ClinicMashhad University of Medical SciencesPublic Health Foundation of IndiaCleveland Clinic FoundationVirginia Commonwealth UniversityNational Nutrition and Food Technology Research InstituteKasturba Medical College, ManipalUniversidade de São PauloKrishna Institute Of Medical Sciences Deemed To Be UniversityUniversity of Nevada, Las VegasZagazig UniversityCharles Sturt UniversityUniversity of SydneyUniversity of Southern CaliforniaBill and Melinda Gates Foundation
KeywordsIncidence (geometry)Rheumatoid arthritisAsthmaAtopic dermatitisInflammatory bowel diseasePsoriasis
DOInot available
Abstract
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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.027
Threshold uncertainty score0.054
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 head0.035
GPT teacher head0.332
Teacher spread0.297 · 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 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
Citations2
Published2023
Admission routes1
Has abstractno