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Record W4224283047 · doi:10.3390/curroncol29040209

Laser Interstitial Thermal Therapy in Grade 2/3 IDH1/2 Mutant Gliomas: A Preliminary Report and Literature Review

2022· review· en· W4224283047 on OpenAlexvenueno aff
Gabrielle W. Johnson, Rowland H. Han, Matthew D. Smyth, Eric C. Leuthardt, Albert H. Kim

Bibliographic record

VenueCurrent Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
FundersAmerican Cancer Society
KeywordsIDH1GliomaMedicineIsocitrate dehydrogenaseCohortMutantInternal medicineOncologyProgression-free survivalOverall survivalCancer researchBiologyGene

Abstract

fetched live from OpenAlex

Laser interstitial thermal therapy (LITT) has become an increasingly utilized alternative to surgical resection for the treatment of glioma in patients. However, treatment outcomes in isocitrate dehydrogenase 1 and 2 (IDH1/2) mutant glioma, specifically, have not been reported. The objective of this study was to characterize a single institution’s cohort of IDH1/2 mutant grade 2/3 glioma patients treated with LITT. We collected data on patient presentation, radiographic features, tumor molecular profile, complications, and outcomes. We calculated progression-free survival (PFS) and tested factors for significant association with longer PFS. Overall, 22.7% of our cohort experienced progression at a median follow up of 1.8 years. The three- and five-year estimates of PFS were 72.5% and 54.4%, respectively. This is the first study to characterize outcomes in patients with IDH1/2 mutant glioma after LITT. Our results suggest that LITT is an effective treatment option for IDH1/2 mutant glioma.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.135
GPT teacher head0.444
Teacher spread0.309 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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