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Record W3210167244 · doi:10.1101/2021.10.28.21265610

COVID-19 Patients’ Symptoms: Gastrointestinal Presentations, Comorbidities and Outcomes in a Canadian Hospital Setting

2021· preprint· en· W3210167244 on OpenAlexaffabout
Hassan Brim, Michal Moshkovich, Melanie Figueiredo, Emily Hartung, Antonio Pizuorno, Lee Hill, Jelena Popov, Eyitope Olaide Awoyemi, Waliul I. Khan, Gholamreza Oskrochi, Hassan Ashktorab, Nikhil Pai

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsPopulation Health Research InstituteMcMaster University
Fundersnot available
KeywordsMedicineComorbidityDiarrheaInternal medicinePsychological interventionPandemicCoronavirus disease 2019 (COVID-19)PediatricsDiseaseInfectious disease (medical specialty)Psychiatry

Abstract

fetched live from OpenAlex

ABSTRACT Background The Coronavirus disease 2019 (COVID-19) pandemic has had significant global impact. While public health interventions and universal health insurance has been credited with minimizing transmission rates in Canada relative to neighboring countries, significant morbidity and mortality have occurred nationwide. We sought to determine factors associated with differences in gastrointestinal outcomes in COVID-19 patients at a Canadian hospital. Methods We collected data from 192 hospital records of COVID-19 patients across seven Hamilton Health Sciences hospitals, a network of academic health centres serving one of the largest metropolitan areas in Canada. Statistical and correlative analysis of symptoms, comorbidities, and mortality were performed. Results There were 192 patients. The mean age was 57.6 years (SD=21.0). For patients who died (n=27, 14%), mean age was 79.2 years old (SD=10.6) versus 54 years for survivors (SD=20.1). There was a higher mortality among patients with older age ( p= 0.000), long hospital stay ( p= 0.004), male patients ( p= 0.032), and patients in nursing homes ( p= 0.000). Patients with dyspnea ( p= 0.028) and hypertension ( p= 0.004) were more likely to have a poor outcome. Laboratory test values that were significant in determining outcomes were an elevated INR ( p= 0.007) and elevated creatinine ( p= 0.000). Cough and hypertension were the most common symptom and comorbidity, respectively. Diarrhea was the most prevalent (14.5%) gastrointestinal symptom. Impaired liver function was related to negative outcome (LR 5.6; p =0.018). Conclusions In a Canadian cohort, elevated liver enzymes, prolonged INR and elevated creatinine were associated with poor prognosis. Hypertension was also linked to a higher likelihood of negative outcome. SUMMARY BOX What is already known about this subject? The prevalence of gastrointestinal symptoms in COVID-19 patients across Canada is lacking Gastrointestinal manifestations of COVID-19 are well described, and longterm sequelae of gastrointestinal tract involvement are an ongoing concern What are the new findings? There was a significant prevalence of gastrointestinal symptoms in patients with a confirmed diagnosis of COVID-19 at one of the largest metropolitan regions across Canada Liver enzyme abnormalities were common in patients at diagnosis This report, over an 8-month period, represents the largest cohort of COVID-19 patients reported in Canada How might these results impact on clinical practice in the foreseeable future? Baseline gastrointestinal symptoms and laboratory abnormalities correlate with patient outcome in Canadian COVID-19 patients These results enhance our knowledge of the prevalence of gastrointestinal symptoms and laboratory abnormalities in Canadian patients and offer important baseline data for longitudinal studies in these patients Our findings increase our knowledge of the epidemiology of COVID-19 in Canada and allow future comparison with international data

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)0.002
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 score0.064
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.387
Teacher spread0.352 · 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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