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Record W2907034986 · doi:10.1002/hed.25378

Surgical anatomy of the parapharyngeal space: Multiperspective, quantification‐based study

2018· article· en· W2907034986 on OpenAlexaff
Marco Ferrari, Alberto Schreiber, Davide Mattavelli, Davide Lombardi, Vittorio Rampinelli, Francesco Doglietto, Luigi Fabrizio Rodella, Piero Nicolai

Bibliographic record

VenueHead & Neck · 2018
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsNeurovascular bundleParapharyngeal spaceMedicineSkullCadaverAnatomyInfratemporal fossaSurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Several surgical approaches to the parapharyngeal space (PPS) have been proposed. An objective description of advantages and limitations of the surgical routes is lacking. METHODS: Ten cadaver heads were dissected using the transnasal (medial, lateral), sublabial, transoral (transpharyngeal, transvestibular, transmandibular), transcervical (transcervical, transparotid, transmandibular, transmastoid), and type C and D infratemporal approaches. Neurovascular and musculoskeletal structures encountered were analyzed. A navigation-based quantification of working volume and exposure of PPS compartments was accomplished. RESULTS: Transnasal approaches exposed the upper PPS, though with limited working volume. Transoral approaches exposed the middle PPS, minimizing neurovascular structures crossed. Only transcervical and skull base approaches exposed the entire PPS, crossing several neurovascular structures. CONCLUSION: A tentative systematization of the surgical approach(es) to PPS in relation to different targets is provided: unicompartmental resection can be performed with a single, conservative access, whereas multicompartmental dissections frequently require a wider or multiportal approach.

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 score0.080
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.386
Teacher spread0.347 · 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

Citations59
Published2018
Admission routes1
Has abstractyes

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