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Record W3111988047 · doi:10.1093/neuonc/noaa215.494

INNV-11. GLIOBLASTOMA IN THE OLDER PERSON – HOW DO WE DECIDE ON TREATMENT?

2020· article· en· W3111988047 on OpenAlexaboutno aff
C. Lorimer, Anthony J. Chalmers, Margaret Johnson, Juliet Brock

Bibliographic record

VenueNeuro-Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGlioblastomaPopulationCohortIncidence (geometry)Test (biology)GerontologyFamily medicineInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Abstract The incidence of glioblastoma (GBM) peaks in the 7th and 8th decades of life. Multiple treatment options exist for older patients with GBM however, the assessment of older patients prior to treatment decisions is poorly researched and lacks standardization. In order to address this issue we performed a cross-sectional electronic survey distributed to all full members of the Society for Neuro-Oncology. There were 116 respondents from a total of 1515 recipients (8% response rate). The survey was distributed during the peak of COVID-19 which undoubtedly affected response rates. 97% of respondents were clinicians with 86% academic. 72% had been in practice > 10 years and the majority saw 5–10 new GBM cases per month. 95% of respondents were from the USA, with involvement from Japan, Australia, Canada and Italy. 37% of respondents routinely perform a cognitive or frailty screening test. Of these, MMSE and MoCA were the most commonly used. Of those who performed a screening test, the majority reported that the results changed their treatment decision in approximately 50% of cases. 50% of respondents have access to a multidisciplinary team during their clinic, with physical therapy being the most available. When making treatment decisions, participants ranked performance status as the most important clinical factor. Considering the heterogeneity of this patient population, we argue that performance status is a crude measure of vulnerability within this cohort. In the first survey of this kind, we have shown a disparity in assessment techniques across the international neuro-oncology field and the impact performing a cognitive screen has on decision making. Older patients with GBM represent a unique clinical scenario because of the complexity of distinguishing neuro- oncology related symptoms from general frailty. There is a need for specific geriatric assessment models tailored to the older neuro-oncology population in order to facilitate treatment decisions.

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)0.018
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.038
GPT teacher head0.299
Teacher spread0.261 · 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
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
Published2020
Admission routes1
Has abstractyes

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