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Record W2802230168 · doi:10.2147/tcrm.s162074

Whole-body computed tomography in trauma patients: optimization of the patient scanning position significantly shortens examination time while maintaining diagnostic image quality

2018· article· en· W2802230168 on OpenAlexaff
Tilman Hickethier, Kamal Mammadov, Bettina Baeßler, Thorsten Lichtenstein, Jochen Hinkelbein, Lucy Smith, Patrick Sven Plum, Seung‐Hun Chon, David Maintz, De‐Hua Chang

Bibliographic record

VenueTherapeutics and Clinical Risk Management · 2018
Typearticle
Languageen
FieldMedicine
TopicRadiation Dose and Imaging
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineImage qualityTrunkArtifact (error)RadiologyNuclear medicineAbdomenArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: The study was conducted to compare examination time and artifact vulnerability of whole-body computed tomographies (wbCTs) for trauma patients using conventional or optimized patient positioning. PATIENTS AND METHODS: Examination time was measured in 100 patients scanned with conventional protocol (Group A: arms positioned alongside the body for head and neck imaging and over the head for trunk imaging) and 100 patients scanned with optimized protocol (Group B: arms flexed on a chest pillow without repositioning). Additionally, influence of two different scanning protocols on image quality in the most relevant body regions was assessed by two blinded readers. RESULTS: Total wbCT duration was about 35% or 3:46 min shorter in B than in A. Artifacts in aorta (27 vs 6%), liver (40 vs 8%) and spleen (27 vs 5%) occurred significantly more often in B than in A. No incident of non-diagnostic image quality was reported, and no significant differences for lungs and spine were found. CONCLUSION: An optimized wbCT positioning protocol for trauma patients allows a significant reduction of examination time while still maintaining diagnostic image quality.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.535
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.315
Teacher spread0.293 · 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

Citations21
Published2018
Admission routes1
Has abstractyes

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