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Record W2055012783 · doi:10.1016/j.otohns.2005.12.007

Intraoperative Cone‐beam CT for Guidance of Temporal Bone Surgery

2006· article· en· W2055012783 on OpenAlexaff
Mark A. Rafferty, Jeffrey H. Siewerdsen, Yvonne Chan, Michael J. Daly, D Moseley, David A. Jaffray, Jonathan C. Irish

Bibliographic record

VenueOtolaryngology · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersNational Cancer Institute
KeywordsCone beam ctCone beam computed tomographyMedicineRadiologyNuclear medicineMedical physicsComputed tomography

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe our preclinical experience with Cone Beam CT (CBCT) in image-guided surgery of the temporal bone. STUDY DESIGN AND SETTINGS: A mobile isocentric C-arm (PowerMobil, Siemens Medical Systems, Erlangen, Germany) modified to include a flat-panel detector (Varian Imaging Products, Palo Alto, CA) and a motorized orbit was developed to acquire multiple projections in rotation about a subject. Initial experiments imaging steel wire in air were used to investigate the system's spatial resolution in 3D image reconstruction. Subsequently temporal bone dissection was performed on five cadaver heads using the modified C-arm as an image guidance system. RESULTS: We obtained a spatial resolution of 0.85 mm. The image acquisition time was 120 seconds and the radiation dose approximately one-tenth of a conventional CT scan. CONCLUSION: CBCT provided submillimeter accuracy at high speed with low radiation dosage to offer utility as an intraoperative imaging system. SIGNIFICANCE: CBCT offers technology that approximates "near-real-time" image guidance. EBM RATING: C-4.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.264
Teacher spread0.255 · 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 designBench or experimental
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

Citations87
Published2006
Admission routes1
Has abstractyes

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