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Record W2316035097 · doi:10.1055/s-0037-1617432

Volumetric Analysis of Endoscopic and Maxillary Swing Surgical Approaches for Nasopharyngectomy

2018· article· en· W2316035097 on OpenAlexaff
Nidal Muhanna, Harley Chan, Jimmy Qiu, Michael J. Daly, Tahsin M. Khan, Francesco Doglietto, Walter Kucharczyk, David S. Goldstein, Jonathan C. Irish, John R. de Almeida

Bibliographic record

VenueJournal of Neurological Surgery Part B Skull Base · 2018
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsToronto General HospitalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineParapharyngeal spaceCone beam computed tomographyCadaverNuclear medicineVolume (thermodynamics)Carotid arteriesRadiologySurgeryComputed tomography

Abstract

fetched live from OpenAlex

Objectives/Hypothesis The endoscopic endonasal approach (EEA) for nasopharyngectomy is an alternative to the maxillary swing approach (MSA) for selected recurrent nasopharyngeal carcinomas (NPC). We compare the access between these approaches. Methods Three cadaver specimens were used to compare access volumes of the EEA and MSA. Exposure volumes were calculated using image guidance registration to cone beam computed tomography and tracking of accessible tissue with volumetric quantification. The area of exposure to the carotid artery was measured. Results The MSA provided higher volumes for access volume compared with the EEA (66.6 vs 39.1 cm3, p = 0.009). The working area was larger in the MSA (80.2 vs 56.9 cm2, p = 0.06). The exposure to the carotid artery was higher in the MSA (1.88 vs 1.62 cm2, p = 0.04). The MSA provided larger volume of exposure for tumors of the parapharyngeal space with exposure below the palate. Conclusions This study suggests that the MSA for nasopharyngectomy provides a larger volume of exposure. However, much of the increased exposure relates to exposure of the parapharyngeal space below the palate. The EEA provides adequate access to superior anatomical structures.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.180
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
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 head0.100
GPT teacher head0.311
Teacher spread0.210 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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