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Record W3014972660 · doi:10.3329/jasbs.v40i1.31728

Measurement of effective dose to patient during interventional cardiac procedure

2014· article· en· W3014972660 on OpenAlexaff
M. M. Mahfuz Siraz, A. Begum, RK Khan, Ashraful Hoque, Amena Begum

Bibliographic record

VenueJournal of the Asiatic Society of Bangladesh Science · 2014
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsAtomic Energy (Canada)
Fundersnot available
KeywordsMedicineFluoroscopyEffective dose (radiation)Radiation exposureCardiac catheterizationNuclear medicineInterventional cardiologyInterventional radiologyRadiation protectionRadiation doseRadiographyRadiologyEmergency medicineSurgery

Abstract

fetched live from OpenAlex

Interventional cardiac procedures result in substantial patient radiation dose due to prolonged fluoroscopy time and radiographic exposure. Patient dose measurement is performed in two catheterization laboratories in Square Hospital Ltd, Dhaka. A total of 50 patients of Square Hospital is included in this study. TLDs are used for the measurement of the dose received by patients during interventional cardiology at Square Hospital, Dhaka. Patients, who underwent CAG, PTCA, and (CAG with PTCA) have average effective dose 3.30 mSv with a range from 0.96 to 9.12 mSv, 24.14 mSv with a range from 7.56 mSv to 56.81 mSv and 25.56 mSv with a range from 1.21 mSv to 95 mSv respectively. Our results correspond well with those obtained by authors in other countries of the world. This study would be useful to establish a database of the patient’s dose for CAG and PTCA. This may lead cardiologists and scientists to adopt necessary safety measures for reducing exposure to patients and occupational workers.J. Asiat. Soc. Bangladesh, Sci. 40(1): 1-7, June 2014

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.257
Teacher spread0.248 · 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

Citations23
Published2014
Admission routes1
Has abstractyes

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