Evidence map›Paper›PMID 38584142›Full record

ArticleRenal failure2024

Perspectives on AI-based recommendations for mask-wearing and COVID-19 vaccination for transplant recipients in the post-COVID-19 era.

Oscar A Garcia Valencia, Charat Thongprayoon, Jing Miao, Jackrapong Bruminhent, Iasmina M Craici, Wisit Cheungpasitporn

Open access · goldAbstract read
In one paragraph

Article in Renal failure, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.7field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Oscar A Garcia ValenciaDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Charat ThongprayoonDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Jing MiaoDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Jackrapong BruminhentDepartment of Medicine, Division of Infectious Diseases, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.
Iasmina M CraiciDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Wisit CheungpasitpornDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
Mayo Clinic · USMahidol University · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the aftermath of the COVID-19 pandemic, the ongoing necessity for preventive measures such as mask-wearing and vaccination remains particularly critical for organ transplant recipients, a group highly susceptible to infections due to immunosuppressive therapy. Given that many individuals nowadays increasingly utilize Artificial Intelligence (AI), understanding AI perspectives is important. Thus, this study utilizes AI, specifically ChatGPT 4.0, to assess its perspectives in offering precise health recommendations for mask-wearing and COVID-19 vaccination tailored to this vulnerable population. Through a series of scenarios reflecting diverse environmental settings and health statuses in December 2023, we evaluated the AI's responses to gauge its precision, adaptability, and potential biases in advising high-risk patient groups. Our findings reveal that ChatGPT 4.0 consistently recommends mask-wearing in crowded and indoor environments for transplant recipients, underscoring their elevated risk. In contrast, for settings with fewer transmission risks, such as outdoor areas where social distancing is possible, the AI suggests that mask-wearing might be less imperative. Regarding vaccination guidance, the AI strongly advocates for the COVID-19 vaccine across most scenarios for kidney transplant recipients. However, it recommends a personalized consultation with healthcare providers in cases where patients express concerns about vaccine-related side effects, demonstrating an ability to adapt recommendations based on individual health considerations. While this study provides valuable insights into the current AI perspective on these important topics, it is crucial to note that the findings do not directly reflect or influence health policy. Nevertheless, given the increasing utilization of AI in various domains, understanding AI's viewpoints on such critical matters is essential for informed decision-making and future research.

Indexed as

COVID-19Artificial IntelligenceCOVID-19 VaccinesHumansPandemicsTransplant RecipientsVaccinationCOVID-19 VaccinesArtificial intelligencecovid-19Mask-Wearing recommendationstransplant recipientsvaccine guidance

Identifiers

PMID38584142
PMCPMC11000603
OpenAlexW4394574918

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.