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Record W3135464768 · doi:10.1093/jcag/gwab002.101

A103 PIVOTING IN A PANDEMIC: THE IMPACT OF COVID-19 ON THE PROVISION OF CARE FOR PATIENTS WITH INFLAMMATORY BOWEL DISEASE: A PRELIMINARY ANALYSIS

2021· article· en· W3135464768 on OpenAlexaffabout
G Malhi, Maria Mikail, Gurjot Minhas, John C. Chambers, Reena Khanna, Aze Wilson

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2021
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseCohortPandemicRetrospective cohort studyDiseaseOutbreakInternal medicineHealth careCohort studyPediatricsCoronavirus disease 2019 (COVID-19)Emergency medicineInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outbreak, also known as COVID-19, was declared a worldwide pandemic in March of 2020. Since the onset of the pandemic, the focus of many healthcare systems has shifted toward limiting non-essential visits to hospitals in order to prioritize and allocate resources toward treating those affected by COVID-19, and preventing further exposures. While the effect of COVID-19 has been felt amongst many patient populations, those with inflammatory bowel disease (IBD) have been particularly impacted through delayed appointments and endoscopy, which is critical in disease monitoring. Aims We aimed to determine how changes to the provision of IBD care due to the COVID-19 pandemic have affected IBD patients. Methods A retrospective cohort study was conducted using administrative data comparing IBD patients admitted to the gastroenterology ward from March 17 to August 31 2019, with IBD patients admitted from March 17 to August 31 2020 at a tertiary care centre in London, Ontario. Patients were reviewed to assess differences in care utilization and IBD-related outcomes such as hospitalization, surgery and length of stay and in-patient drug therapy. Results A total of 538 patients (259 in 2019 cohort and 279 in 2020 cohort) were reviewed with 48 and 60 IBD patients meeting the inclusion criteria for 2019 and 2020 respectively. Patient demographics were similar between 2019 and 2020 cohort for age, sex, rurality, disease type, and biologic exposure. A greater proportion of patients were admitted with IBD flares in 2020 (86.7% vs 75%, p=0.03). Furthermore, the 2020 cohort also had a 45% increase in in-patient surgical consultations (p=0.07), a 50% increase in in-patient IBD-related surgeries (p=0.39), a 69% increase in inpatient Remicade prescription (p=0.13) and a 70% increase in infectious complications at presentation to hospital (p=0.21). A shorter median length of stay was reported for patients in the 2020 cohort (4 days IQR 3.95 vs 5.85 IQR 4.65, p=0.09). Conclusions Preliminary data suggest that during the COVID-19 pandemic, we have seen more deleterious outcomes in our IBD patients such as increased flares necessitating hospital admission. There was also a non-significant trend toward increased infectious complications as well as in-patient surgeries and need for in-patient Remicade. Though these results cannot be fully interpreted due to the need for further sampling, they suggest that IBD patients may be at-risk for poor outcomes in the current climate of medical care. Completion of this study will help define the full impact of care shifts related to reducing the spread of the novel coronavirus on IBD patients and highlight areas of care that need careful assessment and consideration to protect IBD patient health. Funding Agencies None

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)0.001
metaresearch head score (Gemma)0.003
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 score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.315
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 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

Citations0
Published2021
Admission routes2
Has abstractyes

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