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Record W1988788573 · doi:10.5539/gjhs.v7n5p202

Measurement of Entrance Skin Dose and Calculation of Effective Dose for Common Diagnostic X-Ray Examinations in Kashan, Iran

2015· article· en· W1988788573 on OpenAlexvenueno aff
Akbar Aliasgharzadeh, Ehsan Mihandoost, Mahboubeh Masoumbeigi, Morteza Salimian, Mehran Mohseni

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsnot available
FundersKashan University of Medical Sciences
KeywordsMedicineRadiological weaponEffective dose (radiation)Nuclear medicinePelvisRadiographyAbdomenReference doseLumbar spineRadiologyRadiation exposureSurgeryRisk assessment

Abstract

fetched live from OpenAlex

The knowledge of the radiation dose received by the patient during the radiological examination is essential to prevent risks of exposures. The aim of this work is to study patient doses for common diagnostic radiographic examinations in hospitals affiliated to Kashan University of Medical sciences, Iran. The results of this survey are compared with those published by some national and international values. Entrance surface dose (ESD) was measured based on the exposure parameters used for the actual examination and effective dose (ED) was calculated by use of conversion coefficients calculated by Monte Carlo methods. The mean entrance surface dose and effective dose for examinations of the chest (PA, Lat), abdomen (AP), pelvis (AP), lumbar spine (AP, Lat) and skull (AP, Lat) are 0.37, 0.99, 2.01, 1.76, 2.18, 5.36, 1.39 and 1.01 mGy, and 0.04, 0.1, 0.28, 0,28, 0.23, 0.13, 0.01 and 0.01 mSv, respectively. The ESDs and EDs reported in this study, except for examinations of the chest, are generally lower than comparable reference dose values published in the literature. On the basis of the results obtained in this study can conclude that use of newer equipment and use of the proper radiological parameter can significantly reduce the absorbed dose. It is recommended that radiological parameter in chest examinations be revised.

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)0.001
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 score0.132
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.047
GPT teacher head0.371
Teacher spread0.324 · 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

Citations40
Published2015
Admission routes1
Has abstractyes

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