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Record W2064571176 · doi:10.1088/0952-4746/23/2/305

Software for the estimation of foetal radiation dose to patients and staff in diagnostic radiology

2003· article· en· W2064571176 on OpenAlexaff
Ernest Osei, Johnson Darko, K. Faulkner, C J Kotre

Bibliographic record

VenueJournal of Radiological Protection · 2003
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineRadiological weaponPelvisIn uteroAbdomenPregnancyFetusRadiation exposureMedical radiationOccupational exposureObstetricsEffective dose (radiation)RadiologyMedical physicsNuclear medicineMedical emergency

Abstract

fetched live from OpenAlex

Occasionally, it is clinically necessary to perform a radiological examination(s) on a woman who is known to be pregnant or an examination is performed on a woman who subsequently discovers that she was pregnant at the time. In radiological examinations, especially of the lower abdomen and pelvis area, the foetus is directly irradiated. It is therefore important to be able to determine the absorbed dose to the foetus in diagnostic radiology for pregnant patients as well as the foetal dose from occupational exposure of the pregnant worker. The determination of the absorbed dose to the unborn child in diagnostic radiology is of interest as a basis for risk estimates from medical exposure of the pregnant patient and occupational exposure of the pregnant worker. In this paper we describe a simple computer program, FetDose, which calculates the dose to the foetus from both medical and occupational exposures of the pregnant woman. It also calculates the risks of in utero exposure, compares calculated doses with published data in the literature and provides information on the natural spontaneous risks. The program will be a useful tool for the medical and paramedical personnel who are involved with foetal dose (and hence risks) calculations and counselling of pregnant women who may be concerned about in utero exposure of their foetuses.

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)0.001
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.019
GPT teacher head0.280
Teacher spread0.261 · 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.

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

Citations18
Published2003
Admission routes1
Has abstractyes

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