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Record W1965444964 · doi:10.1097/sap.0b013e31822a3ec3

Current Applications of 3-D Intraoperative Navigation in Craniomaxillofacial Surgery

2012· article· en· W1965444964 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

VenueAnnals of Plastic Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicFacial Trauma and Fracture Management
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineNeurosurgeryOtorhinolaryngologyEnophthalmosNavigation systemSurgeryCraniotomyRadiologyOsteotomySurgical planningMedical physicsArtificial intelligenceDiplopia

Abstract

fetched live from OpenAlex

Intraoperative navigation is a tool that provides surgeons with real-time, interactive access to their patient's diagnostic imaging studies while in the operating room. This modality allows for anatomic localization and facilitates intraoperative planning and diagnosis. The application of intraoperative navigation to neurosurgery, otolaryngology, and orthopedic surgery has been well documented; however, only isolated reports have analyzed its potential in the field of craniomaxillofacial surgery. Advancements in 3-dimensional navigational systems have greatly improved the accuracy of the technology, further broadening its scope. In this article, we evaluate a series of 101 craniomaxillofacial cases in which intraoperative navigation was used. The most common application was for intraorbital cases, such as enophthalmos and acute orbital fracture repairs. Other applications included tumor resection, osteotomy design, pathology localization, and craniotomy design. The major limitations of this technology have been its cost and the fact that it cannot reliably be used for soft-tissue reconstruction currently.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.089
Threshold uncertainty score0.428

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.084
GPT teacher head0.347
Teacher spread0.263 · 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