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Record W3168573932 · doi:10.1080/09553002.2021.1931527

Lifetime attributable cancer risk related to prevalent CT scan procedures in pediatric medical imaging centers

2021· article· en· W3168573932 on OpenAlexaff
Atefeh Tahmasebzadeh, Reza Paydar, Mojtaba Soltani‐Kermanshahi, Asghar Maziar, Reza Reiazi

Bibliographic record

VenueInternational Journal of Radiation Biology · 2021
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersIsfahan University of Medical SciencesIran University of Medical Sciences
KeywordsMedicineIncidence (geometry)Computed tomographyAbdomenRadiologyNuclear medicineColorectal cancerAge groupsCancer incidenceCancerInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Evaluation of the organ dose in pediatric patients up to 15 years old and Estimation of lifetime attributable risk (LAR) of cancer incidence in pediatric computed tomography procedures. MATERIALS AND METHODS: Data from 532 patients below 15 years old was collected and they were categorized into four age groups of <1, 1-5, 5-10, and 10-15 years old. NCICT software was used to calculate the organ dose, and LAR of cancer incidence has been estimated according to the BEIR VII report. RESULTS: The highest median dose in all age groups was related to eye lens (head scan), thyroid (chest scan), and colon (abdomen-pelvic scan). The highest average LAR of cancer incidence was observed for breast cancer and colon cancer following a chest CT scan of the youngest group (<1-year-olds) [68.23 per 100,000] and abdomen-pelvic scans of the oldest group (10- to 15-year-olds) [57.30 per 100,000]. CONCLUSION: This study shows that the average LAR is higher in females and it decreases with age in both genders. Although CT scan has an indispensable application in diagnosis, the patient dose should be taken into account before any examination specifically in pediatric patients.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.322
Teacher spread0.315 · 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

Citations29
Published2021
Admission routes1
Has abstractyes

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