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A method of eliminating streak artifacts from metallic dental restorations in CTs of head and neck cancer patients

2001· article· en· W2125174046 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSpecial Care in Dentistry · 2001
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineRadiodensityStreakHead and neck cancerDentistryHead and neckArtifact (error)OrthodonticsRadiographyRadiologySurgeryRadiation therapy

Abstract

fetched live from OpenAlex

The purpose of this report was to develop a method to reduce streak artifacts derived from dental restorations in CT-imaging of the head and neck region. We selected six patients who were being treated for head and neck cancer and who had metal-derived artifacts on their CT that limited pre- or post-surgical visualization. The metal restorations were removed and replaced with non-metallic composite resin restorations. The CT was repeated as required on completion of the procedures. The streak artifacts were completely eliminated in patients in whom radiolucent composite materials were used. Patients have been followed for 6-26 months with no complications or breakdown of the restorations. The substitution of radiolucent for metallic restorations in special cases should be considered as a solution to CT dental artifact problems.

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.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.297
Teacher spread0.284 · 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