MétaCan
Menu
Back to cohort
Record W2007729425 · doi:10.1002/hed.21219

Visualization of anterior skull base defects with intraoperative cone‐beam CT

2009· article· en· W2007729425 on OpenAlexaff
Gideon Bachar, Emma Barker, Harley Chan, Michael J. Daly, S. Nithiananthan, Al Vescan, Jonathan C. Irish, Jeffrey H. Siewerdsen

Bibliographic record

VenueHead & Neck · 2009
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsMount Sinai HospitalUniversity Health NetworkUniversity of TorontoOntario Institute for Cancer ResearchPrincess Margaret Cancer Centre
FundersNational Cancer Institute
KeywordsCribriform plateCadaverCone beam ctSkullCone beam computed tomographyAnatomyLamella (surface anatomy)MedicineVisibilityOblique caseNuclear medicineMaterials scienceBiomedical engineeringRadiologyComputed tomographyOpticsPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: The role of cone-beam CT (CBCT) in demonstrating anterior skull base defects (ASBDs), differing in size and location, was investigated. The study was designed to describe the potential advantage of CBCT in the setting of an intraoperative cerebrospinal fluid (CSF) leak. METHODS: In all, 120 ASBD were evaluated in 5 cadaver heads. Orthogonal and oblique slices were reconstructed. Observer studies assessed the visibility of ASBD in each location as a function of defect size. RESULTS: For 1-, 2-, and 4-mm defects, the percentage that were undetectable ranged from 20% to 33%, 0% to 14%, and 0% to 5%, respectively. Confident breach detection increased with defect size and was most challenging in the lateral lamella and cribriform. CBCT permitted confident detection of ASBD as small as about 2 mm in the fovea ethmoidalis and planum. Oblique views were found to be superior to orthogonal planes. CONCLUSIONS: The ability to identify ASBD depended on the size and location of defect. Oblique viewing planes were optimal for ASBD visualization.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.379

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 head0.018
GPT teacher head0.322
Teacher spread0.303 · 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 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

Citations10
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueHead & NeckSame topicHead and Neck Surgical OncologyFrench-language works237,207