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Record W4386213185 · doi:10.1055/s-0043-1771303

Updates in Robotic Head and Neck Reconstructive Surgery

2023· review· en· W4386213185 on OpenAlex
Michael Hajek, Christopher M. K. L. Yao

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

VenueSeminars in Plastic Surgery · 2023
Typereview
Languageen
FieldMedicine
TopicTracheal and airway disorders
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineTransoral robotic surgeryHead and neckReconstructive surgeryRoboticsHead and neck surgeryLarynxRobotic surgerySurgeryArtificial intelligenceGeneral surgeryRobotOtorhinolaryngologyComputer science

Abstract

fetched live from OpenAlex

The use of robotics in head and neck surgery has drastically increased over the past two decades. Transoral robotic surgery has revolutionized the surgical approach to the upper aerodigestive tract including the oropharynx and supraglottic larynx. The expanded use and improving technology of robotics have allowed for new approaches in both the ablative and reconstructive aspects of head and neck surgery. Here, we discuss the recent updates in robotics in head and neck surgery and future directions the field may turn.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.318
Teacher spread0.257 · 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