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Record W4392741507 · doi:10.53555/sfs.v10i6.2286

Reduction Of CT Pediatric Radiation Dose by Iterative Reconstruction

2023· article· en· W4392741507 on OpenAlexvenueno aff
Baseqah Ghazi Abdulmhsen Alotaibi, Maryam Anowar Ahmed Alhamaid, Naif Abdulaziz Mulath Al Mutairi, Sameer Marzouq Sameer Almutairi, Ayidh Mushabbab Awadh alqahtani, Sultan Nazal Marshed Alenazy

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsnot available
Fundersnot available
KeywordsReduction (mathematics)Radiation doseNuclear medicineMedical physicsMedicineMathematicsGeometry

Abstract

fetched live from OpenAlex

The use of computed tomography (CT) scans in pediatric patients has raised concerns due to the potential risks associated with ionizing radiation exposure. Iterative reconstruction techniques have been developed to reduce radiation dose while maintaining high image quality. This essay explores the effectiveness of iterative reconstruction in reducing radiation dose in pediatric CT scans, highlighting the importance of optimizing dose protocols to ensure the safety of young patients. By reviewing current literature and research studies, this essay aims to provide insights into the benefits and challenges of implementing iterative reconstruction in pediatric radiology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.186

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.109
GPT teacher head0.308
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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