Evidence map›Paper›PMID 37798910›Full record

ArticleJournal of applied clinical medical physics2024

Effects of model size and composition on quality of head-and-neck knowledge-based plans.

Robert Kaderka, Nesrin Dogan, William Jin, Elizabeth Bossart

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

4 authors.

Robert KaderkaDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA.
Nesrin DoganDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA.
William JinDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA.
Elizabeth BossartDepartment of Radiation Oncology, University of Miami Miller School of Medicine, Miami, Florida, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeKnowledge-based planning (KBP) aims to automate and standardize treatment planning. New KBP users are faced with many questions: How much does model size matter, and are multiple models needed to accommodate specific physician preferences? In this study, six head-and-neck KBP models were trained to address these questions.

methodsThe six models differed in training size and plan composition: The KBP

resultsCompared to manual plans, KBP

conclusionsOverall, all models were shown to produce high-quality plans. Differences between model outputs were small compared to the prescription. This indicates only small improvements when increasing model size and minimal influence of the physician when choosing treatment plans for training head-and-neck KBP models.

Indexed as

Radiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-AssistedHumansKnowledge BasesOrgans at RiskRadiometryRadiotherapy Dosageautomated planninghead-and-neckknowledge-based planning

Identifiers

PMID37798910
PMCPMC10860434

What OpenQuestion holds

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LicenceCC BY
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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.