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Record W2052375745 · doi:10.1007/s00247-006-0192-4

Practice of ALARA in the pediatric interventional suite

2006· review· en· W2052375745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePediatric Radiology · 2006
Typereview
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersSociety of Interventional Radiology Foundation
KeywordsMedicineMedical physicsInterventional radiologyQuality assuranceModality (human–computer interaction)Radiation exposureSuiteRadiation protectionNeuroradiologyRadiologyMedical emergencyNuclear medicineComputer science

Abstract

fetched live from OpenAlex

As interventional procedures have become progressively more sophisticated and lengthy, the potential for high patient radiation dose has increased. Staff exposure arises from patient scatter, so steps to minimize patient dose will in turn reduce operator and staff dose. The practice of ALARA in an interventional radiology (IR) suite, therefore, requires careful attention to technical detail in order to reduce patient dose. The choice of imaging modality should minimize radiation when and where possible. In this paper practical steps are outlined to reduce patient dose. Further details are included that specifically reduce operator exposure. Challenges unique to pediatric intervention are reviewed. Reference is made to experience from modern pediatric interventional suites. Given the potential for high exposures, the practice of ALARA is a team responsibility. Various measures are outlined for consideration when implementing a quality assurance (QA) program for an IR service.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.835
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.380
Teacher spread0.341 · 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