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S927 The Impact of the COVID-19 Pandemic on Patients With Ulcerative Colitis: Results From a Global Ulcerative Colitis Patient Survey

2022· article· en· W4316086010 on OpenAlexaffabout
Laurent Peyrin‐Biroulet, Karoliina Ylänne, Allyson Sipes, Michelle Segovia, Sean Gardiner, Joseph C. Cappelleri, Amy Mulvey, Remo Panaccione

Bibliographic record

VenueThe American Journal of Gastroenterology · 2022
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineUlcerative colitisPandemicMedical prescriptionTelehealthFamily medicineQuality of life (healthcare)AnxietyHealth careCoronavirus disease 2019 (COVID-19)DiseaseTelemedicineInternal medicineNursingPsychiatryInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Introduction: The COVID-19 pandemic presented challenges around disease management, lifestyle changes, and provision of care for patients (pts) with ulcerative colitis (UC). Methods: This UC Narrative global survey (United States, Canada, Japan, France, and Finland) was conducted by The Harris Poll between 25 August and 13 December 2021, among 584 pts with UC (confirmed by endoscopy) aged ≥ 18 years who had attended a gastroenterologist or internist’s office in the past 3 years, had not had a colectomy, and had ever taken prescription medication for UC. The survey aimed to understand how the COVID-19 pandemic impacted pts with UC and assessed overall disease management, telehealth use, healthcare experience, perceived quality of care, emotional well-being, reliance on alternative support systems, and preferences for virtual/in-person interactions with doctors. Data were from pts who consented and completed the survey; analyzed using descriptive statistics. Results: Overall, 25% of pts experienced more UC flares during the pandemic than in 2019. Most pts taking prescription medication (88%) were very/somewhat satisfied with their current treatment plan but overall, 53% strongly/somewhat agreed that they were hesitant to change their treatment plan during the pandemic. Factors that pts agreed helped to control UC symptoms included having fewer social outings (37%), working from home (29%), and having less busy schedules (28%). Factors that pts agreed made controlling UC symptoms more difficult included having more anxiety/stress (43%), hesitancy to visit a hospital or office (34%), and being unable to get an appointment with their doctor (23%). Virtual appointments were more common during the pandemic than before, and more pts relied on alternative support systems for management of UC (Table). Overall, 79% were very/somewhat satisfied with their ability to access needed healthcare during the pandemic, and pts who used each appointment type were equally very satisfied/satisfied with the overall quality of care at in-person (81%) and virtual (81%) appointments. However, in-person appointments were preferred by 68% of pts when meeting a new doctor, 55% when experiencing a flare, 52% for regular check-ups, and 21% for UC prescription refills. Conclusion: During the pandemic, most pts with UC were satisfied with their current treatment plan and ability to access healthcare, and more pts relied on alternative support for management of UC, but many were negatively impacted by anxiety/stress. Table 1. - Disease management before, during, and after the COVID-19 pandemic: reliance on alternative support systems for management of ulcerative colitis Prior to the pandemic During the pandemic Plan to do after the pandemic Have never done or plan to do Talked openly with their doctor about how their disease impacts their life 54% 54% 44% 17% Set goals with their doctor for managing their disease 48% 46% 40% 25% Communicated with a nurse at their doctor’s office between appointments 45% 40% 34% 32% Used an online patient portal to contact their doctor’s office or see lab results 31% 47% 33% 32% Used social media to connect with other patients or learn about ulcerative colitis 24% 39% 27% 46% Used symptom tracking or disease management apps 23% 31% 29% 48% Relied on information from patient advocacy groups 19% 27% 22% 54% Relied on patient support groups 15% 22% 22% 59% Had virtual appointments with their doctor 13% 55% 32% 31%

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)0.004
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.351
Teacher spread0.311 · 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".

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Citations1
Published2022
Admission routes2
Has abstractyes

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