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Record W2039885008 · doi:10.1155/2012/594278

CT Scans in Young People in Great Britain: Temporal and Descriptive Patterns, 1993–2002

2012· article· en· W2039885008 on OpenAlexaff
Mark S. Pearce, Jane A Salotti, Nicola Howe, Kieran McHugh, Kwang Pyo Kim, Choonsik Lee, Alan Craft, Amy Berrington de González, Louise Parker

Bibliographic record

VenueRadiology Research and Practice · 2012
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsCancer Care Nova ScotiaDalhousie University
FundersNational Cancer Institute
KeywordsMedicineComputed tomographyAge groupsDemographyRadiologyPediatrics

Abstract

fetched live from OpenAlex

Background. Although using computed tomography (CT) can be greatly beneficial, the associated relatively high radiation doses have led to growing concerns in relation to potential associations with risk of future cancer. Very little has been published regarding the trends of CT use in young people. Therefore, our objective was to assess temporal and other patterns in CT usage among patients aged under 22 years in Great Britain from 1993 to 2002. Methods. Electronic data were obtained from the Radiology Information Systems of 81 hospital trusts within Great Britain. All included patients were aged under 22 years and examined using CT between 1993 and 2002, with accessible radiology records. Results. The number of CT examinations doubled over the study period. While increases in numbers of recorded examinations were seen across all age groups, the greatest increases were in the older patients, most notably those aged 15-19 years of age. Sixty percent of CT examinations were of the head, with the percentages varying with calendar year and patient age. Conclusions. In contrast to previous data from the North of England, the doubling of CT use was not accompanied by an increase in numbers of multiple examinations to the same individual.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.081
GPT teacher head0.395
Teacher spread0.314 · 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

Citations41
Published2012
Admission routes1
Has abstractyes

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