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Record W4361015714 · doi:10.1007/978-3-031-23175-9_16

Precision Medicine in the Treatment of Malignancies Involving the Ventral Skull Base: Present and Future

2023· book-chapter· en· W4361015714 on OpenAlexaff
Marco Ferrari, Stefano Taboni, Giacomo Contro, Piero Nicolai

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicHead and Neck Surgical Oncology
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineParanasal sinusesSkullNasal cavityRadiation therapyEsthesioneuroblastomaDiseaseMelanomaPathologySurgeryCancer research

Abstract

fetched live from OpenAlex

Abstract Cancers involving the ventral skull base are rare and exceedingly heterogeneous. The variety of malignant tumors that arise in the nasal cavity, paranasal sinuses, nasopharynx, and adjacent mesenchymal tissues translates into a proportionally vast spectrum of prognoses, with some histologies such as olfactory neuroblastoma being associated with rare disease-specific death to other histologies such as mucosal melanoma for which survival beyond 5 years is considered a fortunate exception. Parallel to prognosis, treatment of sinonasal cancers is complex, controversial, and deeply dependent upon the putative pretreatment diagnosis. Given their heterogeneity, cancers of the ventral skull base are particularly prone to multidisciplinary management, which is indispensable. The therapeutic options available to date for these cancers include surgery, which currently remains the mainstay of treatment in most cases, along with radiotherapy and chemotherapy. Biotherapy and immunotherapy are only anecdotally and compassionately used. For each histology, a careful selection of modalities and their timing is paramount to ensure the best chance of cure. In keeping with the principles of precision medicine, several nuances displayed by malignancies of the ventral skull base are being considered as treatment-driving characteristics. This current trend arose from the observation that a remarkable variability of behavior can be observed even within a single histology. Although evidence is lacking in this field and several potential customizations of treatment are still at a theoretical level, understanding of these cancers is rapidly evolving and practical applications of this increasing knowledge is the much-needed step forward in the management of such rare cancers. This chapter highlights the tumor characteristics that may serve as treatment-driving factors in the most relevant cancers invading the ventral skull base.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.049
GPT teacher head0.308
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2023
Admission routes1
Has abstractyes

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