Evidence map›Paper›PMID 37693464›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Validating risk prediction models for multiple primaries and competing cancer outcomes in families with Li-Fraumeni syndrome using clinically ascertained data at a single institute.

Nam H Nguyen, Elissa B Dodd-Eaton, Jessica L Corredor, Jacynda Woodman-Ross, Sierra Green, Nathaniel D Hernandez, Angelica M Gutierrez Barrera, Banu K Arun, Wenyi Wang

Open access · greenAbstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed, 0 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Nam H NguyenThe University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX.
Elissa B Dodd-EatonThe University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX.
Jessica L CorredorThe University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX.
Jacynda Woodman-RossThe University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX.
Sierra GreenThe University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX.
Nathaniel D HernandezThe University of Texas MD Anderson Cancer Center, Department of Clinical Cancer Genetics, Houston, TX.
Angelica M Gutierrez BarreraThe University of Texas MD Anderson Cancer Center, Department of Breast Medical Oncology, Houston, TX.
Banu K ArunThe University of Texas MD Anderson Cancer Center, Department of Breast Medical Oncology, Houston, TX.
Wenyi WangThe University of Texas MD Anderson Cancer Center, Department of Bioinformatics and Computation Biology, Houston, TX.
The University of Texas MD Anderson Cancer Center · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
Statistical methods and tools for cancer risk prediction in families with germline mutations in TP53R01CA239342 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI WANG, WENYI · 2019 to 2022
$1.5M
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA239342
6 · The paper itself

Abstract

Purpose: There exists a barrier between developing and disseminating risk prediction models in clinical settings. We hypothesize this barrier may be lifted by demonstrating the utility of these models using incomplete data that are collected in real clinical sessions, as compared to the commonly used research cohorts that are meticulously collected. Patients and methods: Genetic counselors (GCs) collect family history when patients (i.e., probands) come to MD Anderson Cancer Center for risk assessment of Li-Fraumeni syndrome, a genetic disorder characterized by deleterious germline mutations in the Results: For prediction of deleterious Conclusion: We describe a study that fills in the critical gap in knowledge for the utility of risk prediction models. Using a CCB cohort, our previously validated models have demonstrated good performance and outperformed the standard clinical criteria. Our study suggests better risk counseling may be achieved by GCs using these already-developed mathematical models.

Identifiers

PMID37693464
PMCPMC10491358
OpenAlexW4386405663

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.