Evidence map›Paper›PMID 38729212›Full record

ArticlePhysics in medicine and biology2024

Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates.

Joel A Pogue, Joseph Harms, Carlos E Cardenas, Xenia Ray, Natalie Viscariello, Richard A Popple, Dennis N Stanley, D Hunter Boggs

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Physics in medicine and biology, 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
–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

3 citing papers in PubMed.

  1. Article
  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

8 authors.

Joel A PogueDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America.ORCID 0000-0003-4450-6698
Joseph HarmsDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America.ORCID 0000-0003-2441-9853
Carlos E CardenasDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America.
Xenia RayDepartment of Radiation Medicine and Applied Sciences, University of California San Diego, San Diego, CA, United States of America.
Natalie ViscarielloDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America.
Richard A PoppleDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America.
Dennis N StanleyDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America.
D Hunter BoggsDepartment of Radiation Oncology, University of Alabama at Birmingham, Birmingham, AL, United States of America.

Funding

CTSA K12 Program at the University of Alabama at BirminghamK12TR004769 · NCATS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Renee A. Heffron, Lucio Miele · 2024 to 2026
$4.8M
NCATS NIH HHS K12 TR004769NCI NIH HHS L30 CA284339
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Breast NeoplasmsMachine LearningRadiotherapy Planning, Computer-AssistedFemaleHumansRadiosurgeryaccelerated partial breast irradiationadaptive radiotherapyCBCT-based adaptionearly-stage breast cancermachine learningplan comparison

Identifiers

PMID38729212
PMCPMC11412112

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.