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Record W4384108341 · doi:10.1093/noajnl/vdad071.012

THE USE OF 5-AMINOLEVULINIC ACID (5-ALA) IN HIGH-GRADE GLIOMA SURGERY, A SINGLE CANADIAN CENTER EXPERIENCE

2023· article· en· W4384108341 on OpenAlexaffabout
Félix Leblanc, Lyndon Boone, Timothy Noble, Jane C. Burns, Charbel Fawaz, Dhany Charest, Antonios El Helou

Bibliographic record

VenueNeuro-Oncology Advances · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineGliomaAdverse effectSurgerySingle CenterResectionPathologicalCohortLog-rank testSurvival analysisInternal medicine

Abstract

fetched live from OpenAlex

Abstract 5-Aminolevulinic acid (5-ALA) is a prodrug used to selectively illuminate high-grade glioma (HGG) tissue intra-operatively, shown to nearly double complete resection rates in a 2006 multicentre, phase III clinical trial. Here, we review the history of the 2020 approval of 5-ALA in Canada and present some of the first preliminary results on resection rates, survival analysis, and adverse effects from a single Canadian center. METHODS: We enrolled 76 patients (median age 61 years, 42 male) with suspected HGG amenable to surgical resection to undergo 5-ALA fluorescence-guided surgery between June 2020 and January 2023. Gross total resection was defined by the absence of enhancing l esions on postoperative MRI. We compared the survival distributions of confirmed HGG cases with complete vs. incomplete resection using a log-rank test and Kaplan-Meier statistics. RESULTS: 52 patients were confirmed as having a HGG (grade III or IV) based on a pathological diagnosis. In 32 of these patients (60.3%) a gross total resection was achieved. 46 patients of the initial cohort had their surgery done for more than 180 days. 47.8% had a survival of 600 or more days. CONCLUSIONS: 5-ALA fluorescence-guided surgery resulted in high complete resection rates, comparable to literature with no notable adverse side effects.

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)0.001
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 score0.443
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.074
GPT teacher head0.320
Teacher spread0.247 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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