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Record W4378348202 · doi:10.3390/curroncol30050377

Surgical Management of Brain Tumors with Focused Ultrasound

2023· review· en· W4378348202 on OpenAlexvenueno aff
Yusuf Mehkri, Kevin Pierre, Samuel Woodford, Caroline Davidson, Ogaga Urhie, Sai Sriram, Jairo Hernandez, Chadwin Hanna, Brandon Lucke‐Wold

Bibliographic record

VenueCurrent Oncology · 2023
Typereview
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCraniotomyFocused ultrasoundUltrasoundThermal ablationClinical trialRadiologyImmunotherapyGliomaAblationSurgeryPathologyCancerInternal medicineCancer research

Abstract

fetched live from OpenAlex

Focused ultrasound is a novel technique for the treatment of aggressive brain tumors that uses both mechanical and thermal mechanisms. This non-invasive technique can allow for both the thermal ablation of inoperable tumors and the delivery of chemotherapy and immunotherapy while minimizing the risk of infection and shortening the time to recovery. With recent advances, focused ultrasound has been increasingly effective for larger tumors without the need for a craniotomy and can be used with minimal surrounding soft tissue damage. Treatment efficacy is dependent on multiple variables, including blood-brain barrier permeability, patient anatomical features, and tumor-specific features. Currently, many clinical trials are currently underway for the treatment of non-neoplastic cranial pathologies and other non-cranial malignancies. In this article, we review the current state of surgical management of brain tumors using focused ultrasound.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.124
GPT teacher head0.390
Teacher spread0.266 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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