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Record W4386538594 · doi:10.1093/neuonc/noad137.403

P17.13.A OUTCOME ANALYSIS OF PATIENTS WITH RECURRENT HIGH-GRADE GLIOMAS TREATED WITH ORAL ETOPOSIDE (VP16) IN ALBERTA, CANADA: A POPULATION-BASED RETROSPECTIVE STUDY

2023· article· en· W4386538594 on OpenAlexaffabout
Yuan Gao, Chétana Lim, Sunita Ghosh, Frances Folkman, Gloria Roldan Urgoiti

Bibliographic record

VenueNeuro-Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineEtoposideInternal medicineProportional hazards modelGliomaRetrospective cohort studyPopulationSurvival analysisLog-rank testProgression-free survivalOligodendrogliomaSurgeryOverall survivalChemotherapyAstrocytoma

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Tumour recurrence in patients with gliomas is associated with short survival and there is no standard second-line therapy. Etoposide (VP16) taken orally is one of the systemic treatment options available with an acceptable safety profile in this patient population. MATERIAL AND METHODS We conducted a retrospective analysis of patients who received oral VP16 as monotherapy for the management of recurrent/progressive gliomas in Alberta, Canada 2009 - 2019 with the aim to identify the patients’ clinical and histological characteristics associated with improved outcomes with VP16. Eligible patients were identified using the provincial pharmacy records. As progression free survival (PFS) in second line treatment in these patients is < 3 months we defined as “benefiting” from VP16 those patients that completed 4 or more cycles/months of VP16. Two-tailed χ2 or Fisher’s exact tests were used to determine the significance of associations between proportions. Cox proportional hazards model and Kaplan-Meier survival analysis with log-rank test were used to determine factors associated with Overall Survival (OS) and PFS. RESULTS At the time of current abstract, data abstraction was completed for 200 out of 350 identified patients. Partial data analysis were performed on 197 patients who received at least one cycle of VP16 for recurrent glioma; 55 patients (28%) completed 4 or more cycles of VP16; patients with oligodendroglioma were 60% more likely to benefit while patients with glioblastoma were 1.56 times more likely to stop before 4 months of treatment. Patients that received 4 or more cycles of VP16 were younger (p=0.041); compared to patients > 61 years old, patients 21-31 and 41-61 years old were 82% and 29% less likely to stop before cycle 4, respectively (OR 0.18 and 0.71). Median OS from diagnoses for patients that did not benefit from VP16 was 22 (95% CI; 15.7 to 28.3) months compared to 45.7 (95% CI; 31.8 to 59.5) months in patients that were able to receive at least 4 cycles of VP16 (p=0.006). However, the proportion IDH mutated tumors, MGMT promoter methylation and use of dexamethasone at start of treatment did not differ between patients that benefit or not from VP16. As expected, lower KPS (p=0.007) and diagnoses of glioblastoma (p=0.001) were associated with worse OS. Better OS and PFS were associated with MGMT promoter methylation (p<0.0001 and p=0.030, respectively), 1p/19q codel (p=0.042 and p=0.018, respectively), and IDH mutation status (p<0.0001 and p=0.022, respectively). CONCLUSION In this retrospective series of patients with recurrent gliomas that started VP16 more than 1/4 of the patients received 4 or more cycles/months of treatment. Younger patients and those with oligodendrogliomas were more likely to benefit. We consider VP16 at 50 mg/day po a valid option for patients whose performance status would allow trying an additional line of palliative systemic treatment.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.140
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.289
Teacher spread0.271 · 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 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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