Evidence map›Paper›PMID 39070248›Full record

ArticleProceedings. IEEE International Conference on Healthcare Informatics2023

Exploring the Effect of Eligibility Criteria on AD Severity and Severe Adverse Event in Eligible Patients.

Aokun Chen, Qian Li, Elizabeth Shenkman, Yonghui Wu, Yi Guo, Jiang Bian

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings. IEEE International Conference on Healthcare Informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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.

Aokun ChenDepartment of Health Outcome and Biomedical Informatics, College of Medicine University of Florida Gainesville, FL,USA.
Qian LiDepartment of Health Outcome and Biomedical Informatics, College of Medicine University of Florida Gainesville, FL,USA.
Elizabeth ShenkmanDepartment of Health Outcome and Biomedical Informatics, College of Medicine University of Florida Gainesville, FL,USA.
Yonghui WuDepartment of Health Outcome and Biomedical Informatics, College of Medicine University of Florida Gainesville, FL,USA.
Yi GuoDepartment of Health Outcome and Biomedical Informatics, College of Medicine University of Florida Gainesville, FL,USA.
Jiang BianDepartment of Health Outcome and Biomedical Informatics, College of Medicine University of Florida Gainesville, FL,USA.

Funding

Optimizing the Population Representativeness of Older Adults in Alzheimer's Disease and Related Dementia Clinical TrialsR21AG068717 · NIA · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2021
$419k
Optimizing the Population Representativeness of Older Adults in Cancer TrialsR21CA253394 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2021 to 2021
$392k
NCI NIH HHS R21 CA253394NIA NIH HHS R21 AG068717
6 · The paper itself

Abstract

Clinical trials were vital tools to prove the effectiveness and safety of medications. To maximize generalizability, the study sample should represent the sample population and the target population. However, the clinical trial design tends to favor the evaluation of drug safety and procedure (i.e., internal validity) without clear knowledge of its penalty on trial generalizability (i.e., external validity). Alzheimer's Disease (AD) trials are known to have generalizability issues. Thus, in this study, we explore the effect of eligibility criteria on the AD severity patients and the severe adverse event (SAE) among the eligible patients.

Indexed as

clinical trialeligibility criteriamachine learningSHAP value

Identifiers

PMID39070248
PMCPMC11273173

What OpenQuestion holds

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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.