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When is a multidisciplinary surgical approach required in sinonasal tumours with cranial involvement?

2021· article· en· W3160058403 on OpenAlexaff
Marco Ferrari, Diego Cazzador, Stefano Taboni, Maria Vittoria Trimarchi, Enzo Emanuelli, Piero Nicolai

Bibliographic record

VenueActa Otorhinolaryngologica Italica · 2021
Typearticle
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineMultidisciplinary approachMultidisciplinary teamOtorhinolaryngologyOtologyPerioperativeSurgeryGeneral surgery

Abstract

fetched live from OpenAlex

The term “sinonasal tumours” includes a large spectrum of diseases, which are characterized by heterogeneous biological behavior and prognosis, and located in a critical anatomic area. Diagnosis and treatment of sinonasal tumours require the contribution of different disciplines. A narrative review was performed to highlight the role of surgeons in contributing to a multidisciplinary approach to sinonasal tumours. Diagnosis and staging of sinonasal tumours is challenging and requires collaboration between surgeons, radiologists, and pathologists. The identification and management of critical extensions (orbital or intracranial encroachment, vascular abutment or encasement) is fundamental for successful treatment. Most cases of advanced sinonasal tumours can undergo surgical intervention by an adequately trained otorhinolaryngological team. The contribution of neurosurgeons and oculoplastic surgeons is required in selected scenarios. In rare circumstances, multidisciplinary reconstructive strategies can be indicated for complex tissue defects. Furthermore, a multidisciplinary approach is pivotal in the management of perioperative complications. While surgery remains the mainstay of treatment, the role of non-surgical adjuvant or even exclusive treatments is constantly expanding.

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 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 score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.031
GPT teacher head0.287
Teacher spread0.256 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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