Background Large language models such as ChatGPT can produce increasingly realistic text, with unknown information on the accuracy and integrity of using these models in scientific writing. Methods We gathered ten research abstracts from five high impact factor medical journals (n=50) and asked ChatGPT to generate research abstracts based on their titles and journals. We evaluated the abstracts using an artificial intelligence (AI) output detector, plagiarism detector, and had blinded human reviewers try to distinguish whether abstracts were original or generated. Results All ChatGPT-generated abstracts were written clearly but only 8% correctly followed the specific journal’s formatting requirements. Most generated abstracts were detected using the AI output detector, with scores (higher meaning more likely to be generated) of median [interquartile range] of 99.98% [12.73, 99.98] compared with very low probability of AI-generated output in the original abstracts of 0.02% [0.02, 0.09]. The AUROC of the AI output detector was 0.94. Generated abstracts scored very high on originality using the plagiarism detector (100% [100, 100] originality). Generated abstracts had a similar patient cohort size as original abstracts, though the exact numbers were fabricated. When given a mixture of original and general abstracts, blinded human reviewers correctly identified 68% of generated abstracts as being generated by ChatGPT, but incorrectly identified 14% of original abstracts as being generated. Reviewers indicated that it was surprisingly difficult to differentiate between the two, but that the generated abstracts were vaguer and had a formulaic feel to the writing. Conclusion ChatGPT writes believable scientific abstracts, though with completely generated data. These are original without any plagiarism detected but are often identifiable using an AI output detector and skeptical human reviewers. Abstract evaluation for journals and medical conferences must adapt policy and practice to maintain rigorous scientific standards; we suggest inclusion of AI output detectors in the editorial process and clear disclosure if these technologies are used. The boundaries of ethical and acceptable use of large language models to help scientific writing remain to be determined.

/pdf/comparing-scientific-abstracts-generated-by-chatgpt-to-1qt5ziht.pdf

Comparing scientific abstracts generated by ChatGPT to original abstracts using an artificial intelligence output detector, plagiarism detector, and blinded human reviewers

Objectives: Although COVID-19 vaccines can reduce the need for intensive care unit admission in COVID-19, their effect on outcomes in critical illness remains unclear. We evaluated outcomes in vaccinated patients admitted to the ICU with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the association between vaccination and booster status on clinical outcomes. Design: Retrospective cohort. Setting and Patients: All patients were admitted to an ICU between January 2021 (after vaccination was available) and July 2022 with a diagnosis of COVID-19 based on a SARS-CoV-2 polymerase chain reaction test in Alberta, Canada. Interventions: None. Measurement: The propensity-matched primary outcome of all-cause in-hospital mortality was compared between vaccinated and unvaccinated patients, and vaccinated patients were stratified by booster dosing. Secondary outcomes were mechanical ventilation (MV) duration ICU length of stay (LOS). Main Results: The study included 3,293 patients: 743 (22.6%) were fully vaccinated (54.6% with booster), 166 (5.0%) were partially vaccinated, and 2,384 (72.4%) were unvaccinated. Unvaccinated patients were more likely to require invasive MV (78.4% vs 68.2%), vasopressor use (71.1% vs 66.6%), and extracorporeal membrane oxygenation (2.1% vs 0.5%). In a propensity-matched analysis, in-hospital mortality was similar (31.8% vs 34.0%, adjusted odds ratio [OR], 1.25; 95% CI, 0.97–1.61), but median duration MV (7.6 vs 4.7 d; p < 0.001) and ICU LOS (6.6 vs 5.2 d; p < 0.001) were longer in unvaccinated compared to fully vaccinated patients. Among vaccinated patients, greater than or equal to 1 booster had lower in-hospital mortality (25.5% vs 40.9%; adjusted OR, 0.50; 95% CI, 0.0.36–0.68) and duration of MV (3.8 vs 5.6 d; p = 0.025). Conclusions: Nearly one in four patients admitted to the ICU with COVID-19 after widespread COVID-19 vaccine availability represented a vaccine-breakthrough case. Mortality risk remains substantial in vaccinated patients and similar between vaccinated and unvaccinated patients after the onset of critical illness. However, COVID-19 vaccination is associated with reduced ICU resource utilization and booster dosing may increase survivability from COVID-19-related critical illness. 

Association Between Vaccination Status and Outcomes in Patients Admitted to the ICU With COVID-19

Background
Vaccines against coronavirus disease-19 (COVID-19) have been effective in preventing symptomatic diseases, hospitalizations, and intensive care unit (ICU) admissions. However, data regarding the effectiveness of COVID-19 vaccines in reducing mortality among critically ill patients with COVID-19 remains unclear.


Aims
To determine the vaccination status and investigate the impact of the COVID-19 vaccine on the 28-day mortality in critically ill patients with COVID-19.


Study Design
Multicenter prospective observational clinical study.


Methods
This study was conducted in 60 hospitals with ICUs managing critically ill patients with COVID-19. Patients aged ≥ 18 years with confirmed COVID-19 who were admitted to the ICU were included. The present study had two phases. The first phase was designed as a one-day point prevalence study, and demographic and clinical findings were evaluated. In the second phase, the 28-day mortality was evaluated.


Results
As of August 11, 2021, 921 patients were enrolled in the study. The mean age of the patients was 65.42 ± 16.74 years, and 48.6% (n = 448) were female. Among the critically ill patients with COVID-19, 52.6% (n = 484) were unvaccinated, 7.7% (n = 71) were incompletely vaccinated, and 39.8% (n = 366) were fully vaccinated. A subgroup analysis of 817 patients who were unvaccinated (n = 484) or who had received two doses of the CoronaVac vaccine (n = 333) was performed. The 28-day mortality rate was 56.8% (n = 275) and 57.4% (n = 191) in the unvaccinated and two-dose CoronaVac groups, respectively. The 28-day mortality was associated with age, hypertension, the number of comorbidities, type of respiratory support, and APACHE II and sequential organ failure assessment scores (p < 0.05). The odds ratio for the 28-day mortality among those who had received two doses of CoronaVac was 0.591 (95% confidence interval: 0.413-0.848) (p = 0.004).


Conclusion
Vaccination with at least two doses of CoronaVac within six months significantly decreased mortality in vaccinated patients than in unvaccinated patients.

The Impact of CoronaVac Vaccination on 28-day Mortality Rate of Critically Ill Patients with COVID-19 in Türkiye

INTRODUCTION
An excessive use of antibiotics has been frequently reported in hospitalized patients with COVID-19 worldwide, compared to the actual number of bacterial co-infections or super-infections.


AREAS COVERED
In this perspective, we discuss the current literature on the use of antibiotics and antimicrobial stewardship interventions in hospitalized patients with COVID-19. A search was conducted in PubMed up to March 2023.


EXPERT OPINION
The COVID-19 pandemic has witnessed an excessive use of antibiotics in hospital worldwide, especially before the advent of COVID-19 vaccination, although according to most recent data there still is an important disproportion between the prevalence of antibiotic use and that of proven bacterial coinfection or superinfections. An important reduction in the prevalence of antibiotic use in COVID-19 patients reported in the literature, from 70-100% to 50-60%, has been observed after successful vaccination campaigns, likely related to the reduced median severity of hospitalized COVID-19 patients and some successful interventions of antimicrobial and diagnostic stewardship. However, the disproportion between antibiotic use and the prevalence of bacterial infections (4-6%) is still uncomfortable from an antimicrobial stewardship perspective and requires further attention.

Use of antibiotics in hospitalized patients with COVID-19: evolving concepts in a highly dynamic antimicrobial stewardship scenario.

In COVID-19 patients, antibiotics overuse is still an issue. A predictive scoring model for the diagnosis of bacterial pneumonia at intensive care unit (ICU) admission would be a useful stewardship tool. We performed a multicenter observational study including 331 COVID-19 patients requiring invasive mechanical ventilation at ICU admission; 179 patients with bacterial pneumonia; and 152 displaying negative lower-respiratory samplings. A multivariable logistic regression model was built to identify predictors of pulmonary co-infections, and a composite risk score was developed using β-coefficients. We identified seven variables as predictors of bacterial pneumonia: vaccination status (OR 7.01; 95% CI, 1.73-28.39); chronic kidney disease (OR 3.16; 95% CI, 1.15-8.71); pre-ICU hospital length of stay ≥ 5 days (OR 1.94; 95% CI, 1.11-3.4); neutrophils ≥ 9.41 × 109/L (OR 1.96; 95% CI, 1.16-3.30); procalcitonin ≥ 0.2 ng/mL (OR 5.09; 95% CI, 2.93-8.84); C-reactive protein ≥ 107.6 mg/L (OR 1.99; 95% CI, 1.15-3.46); and Brixia chest X-ray score ≥ 9 (OR 2.03; 95% CI, 1.19-3.45). A predictive score (C19-PNEUMOSCORE), ranging from 0 to 9, was obtained by assigning one point to each variable, except from procalcitonin and vaccine status, which gained two points each. At a cut-off of ≥3, the model exhibited a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 84.9%, 55.9%, 69.4%, 75.9%, and 71.6%, respectively. C19-PNEUMOSCORE may be an easy-to-use bedside composite tool for the early identification of severe COVID-19 patients with pulmonary bacterial co-infection at ICU admission. Its implementation may help clinicians to optimize antibiotics administration in this setting. 

An Observational Study to Develop a Predictive Model for Bacterial Pneumonia Diagnosis in Severe COVID-19 Patients—C19-PNEUMOSCORE

Key Points Question Does COVID-19 vaccination prevent intensive care unit (ICU) admission for COVID-19 pneumonia and improve patient outcomes? Findings In this cohort study of more than 10 million people, vaccines based on mRNA technology or adenoviral vectors significantly decreased the risk of ICU admission for COVID-19 pneumonia. ICU and hospital mortality, adjusted for age, heart disease and Pao2/FiO2 at ICU admission, were similar between vaccinated and unvaccinated patients. Meaning COVID-19 vaccines were associated with a lower ICU admission for COVID-19 pneumonia, while no significant association was detected with ICU and hospital mortality.

/pdf/association-of-covid-19-vaccinations-with-intensive-care-1zpc98h0.pdf

Association of COVID-19 Vaccinations With Intensive Care Unit Admissions and Outcome of Critically Ill Patients With COVID-19 Pneumonia in Lombardy, Italy

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Association of COVID-19 Vaccinations With Intensive Care Unit Admissions and Outcome of Critically Ill Patients With COVID-19 Pneumonia in Lombardy, Italy