MétaCan
Menu
Back to cohort
Record W2005278501 · doi:10.5402/2013/204346

A Survey of Organ Equivalent and Effective Doses from Diagnostic Radiology Procedures

2012· article· en· W2005278501 on OpenAlexaff
Ernest Osei, Johnson Darko

Bibliographic record

VenueISRN Radiology · 2012
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsQueen's UniversityKingston General HospitalGrand River HospitalUniversity of Waterloo
FundersJapan Society for the Promotion of Science London
KeywordsMedicineRadiological weaponEffective dose (radiation)Nuclear medicineAbdomenRadiographyRadiologyPelvisRadiation exposure

Abstract

fetched live from OpenAlex

The quantification of radiation risks associated with radiological examinations has been a subject of interest with the increased use of X-rays. Effective dose, which is a risk-weighted measure of radiation to organs in the body associated with radiological examination, is considered a good indicator of radiological risk. We have therefore investigated patient effective doses from radiological examinations. Organ and effective doses were estimated for 94 patients who underwent computed tomography examinations and for 338 patients who had conventional radiography examinations. The OrgDose (version 2) program was used for the estimation of effective doses. The tube potential ranges: 57 kVp to 138 kVp depending on the examination and patient size. The entrance surface doses have a wide range even for the same examination: 0.44-10.31 mGy (abdomen) and 0.66-16.08 mGy (lumbar spine) and the corresponding effective dose ranges 0.025-0.77 mSv and 0.025-0.95 mSv respectively. Effective dose for adult abdomen-pelvic CT examinations ranges 5.4-19.8 mSv with a mean of 13.6 mSv and for pediatrics ranges 2.1-5.5 mSv with a mean of 2.7 mSv. The mean effective dose for adult chest and head CT examinations are 7.9 and 1.8 mSv respectively and for pediatrics are 1.7 and 1.1 mSv.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.297
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), 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

Citations67
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueISRN RadiologySame topicRadiation Dose and ImagingFrench-language works237,207