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Record W4243995945 · doi:10.1055/s-0038-1633627

Quantitative Analysis of Surgical Working Space during Endoscopic Skull Base Surgery

2018· article· en· W4243995945 on OpenAlexaff
Joel Davies, Harley Chan, Christopher M. K. L. Yao, Michael D. Cusimano, J.D. Irish

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2018
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSkullEndoscopeSpace (punctuation)Base (topology)VisualizationMedicineEndoscopyComputer scienceSurgeryArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Background The transsphenoidal endoscopic approach has widely been accepted as standard practice for accessing the majority of tumors of the skull base. Over the years, varying approaches have been described to improve visualization and maximize working space within the sinonasal corridor. Specifically, the approach may include a limited or wide posterior septectomy and the partial resection of one or both middle turbinates. No study to date has quantitatively assessed the improvement in the overall distance of working space, and distance between instruments/endoscope with these various maneuvers. Our study sought to calculate these measurements and determine the sequential quantitative improvement in working space.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.080
GPT teacher head0.326
Teacher spread0.247 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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