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Record W3046063580 · doi:10.1093/ecco-jcc/jjaa150

Care of the Patient With IBD Requiring Hospitalisation During the COVID-19 Pandemic

2020· article· en· W3046063580 on OpenAlexaff
Matthieu Allez, Phillip Fleshner, Richard B. Gearry, Péter L. Lakatos, David T. Rubin

Bibliographic record

VenueJournal of Crohn s and Colitis · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsMcGill University
FundersEuropean Crohn's and Colitis Organisation
KeywordsCoronavirus disease 2019 (COVID-19)PandemicMedicine2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Intensive care medicineVirologyEmergency medicineInternal medicineInfectious disease (medical specialty)DiseaseOutbreak

Abstract

fetched live from OpenAlex

The management of IBD has been highly affected in the context of the COVID-19 pandemic, with restriction of hospitalisations and unprecedented redeployment of health care resources. Hospital admissions of IBD patients should be limited to reduce the risks of coronavirus transmission. However, delaying hospitalisation of IBD patients with severe or complicated disease may increase the risk of poor outcomes. Delaying surgery in some cases may increase the risk of disease progression, postoperative morbidity, and disease complications. IBD patients who are infected with SARS-CoV-2 may have a higher risk of poor outcomes than the general population, potentially related to concomitant medications, especially corticosteroids. There is no evidence today that IBD patients with COVID-19 have worse outcomes if they receive immunosuppressant medications including thiopurines, biologics, and novel small molecules. This article summarises recommendations by the international membership of IOIBD regarding hospitalisations of IBD patients, either for active or complicated IBD or for severe COVID-19, and for management of IBD patients according to SARS-CoV-2 infectious status.

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 score0.065
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.330
GPT teacher head0.463
Teacher spread0.133 · 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

Citations30
Published2020
Admission routes1
Has abstractyes

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