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Record W1967349379 · doi:10.1016/j.otohns.2008.12.046

Intraoperative use of cone‐beam computed tomography in a cadaveric ossified cochlea model

2009· article· en· W1967349379 on OpenAlexaff
Emma Barker, Keith Trimble, Harley Chan, James D. Ramsden, S. Nithiananthan, Adrian L. James, Gideon Bachar, Jonathan C. Irish, J. H. Siewerdsen

Bibliographic record

VenueOtolaryngology · 2009
Typearticle
Languageen
FieldMedicine
TopicEar Surgery and Otitis Media
Canadian institutionsUniversity Health NetworkHospital for Sick ChildrenPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Cancer Institute
KeywordsCadaveric spasmCone beam computed tomographyCochleaTemporal boneMedicineCochlear implantCone beam ctElectrode arrayAnatomyRadiologyComputed tomographyElectrodeAudiology

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe a cadaveric temporal bone model of labyrinthitis ossificans and investigate the utility of intraoperative cone-beam computed tomography (CBCT) in the facilitating cochlear implantation. DESIGN: Cadaveric temporal bone study. METHODS: Five cadaveric heads had cement introduced into the 10 cochleas. CBCT and a conventional CT scan were compared to assess the extent of cochlear obliteration. The cement was drilled-out (under CBCT guidance, if required) and cochlear implant electrode arrays (from 3 different manufacturers) inserted. RESULTS: CBCT images demonstrated temporal bone anatomy and the extent of cochlear obliteration as clearly as conventional CT in all cases. Intraoperative CBCT guided drilling and facilitated electrode placement in two of five heads (3 of 10 ears). Streak-artifact from the electrodes of two devices partially obscured image clarity. CONCLUSIONS: The obliterated cochlear model reproduced a disease-ossified cochlear both radiographically and surgically. CBCT is useful for intraoperative imaging to facilitate electrode array placement in the obliterated or congenitally abnormal cochlea.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.029
GPT teacher head0.262
Teacher spread0.233 · 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

Citations40
Published2009
Admission routes1
Has abstractyes

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