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Record W3108010620 · doi:10.21037/apm-20-1206

Update on the management of elderly patients with glioblastoma: a narrative review

2020· review· en· W3108010620 on OpenAlexaff
Sarah Ironside, Arjun Sahgal, Jay Detsky, Sunit Das, James Perry

Bibliographic record

VenueAnnals of Palliative Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsSt. Michael's HospitalSunnybrook Health Science CentreMoncton HospitalUniversity of TorontoDalhousie UniversityOccupational Cancer Research CentreHealth Sciences Centre
Fundersnot available
KeywordsMedicineTemozolomidePsychosocialQuality of life (healthcare)Randomized controlled trialRegimenGlioblastomaRadiation therapyInternal medicineClinical trialOncologyIntensive care medicinePsychiatryNursing

Abstract

fetched live from OpenAlex

Glioblastoma in the elderly (>65 years of age) is associated with shorter overall survival (OS) than in younger patients. Best practice recommendations for elderly patients, especially those with borderline or poor performance status, remain a subject of debate amongst clinicians despite recent randomized trials. This review provides an updated evidence-based summary to inform the modern management of elderly patients with glioblastoma. Based on evidence from the CE.6 randomized controlled trial, hypofractionated radiation therapy administered over a three-week course (40 Gy in 15 fractions) concomitantly with temozolomide (TMZ) followed by adjuvant TMZ has been found to be superior to radiation therapy alone with mean OS of 9.3 vs. 7.6 months and progression-free survival (PFS) of 5.3 vs. 3.9 months. This regimen should be offered to newly-diagnosed elderly patients with glioblastoma with preserved functional status, and was not associated with a negative impact on health-related quality of life (QOL). Management of elderly patients with glioblastoma can be challenging and requires a patient-centered strategy. Personalized decisions accounting for clinical, psychosocial, molecular and treatment factors are critical for realistic decision making. The importance of discussing goals-of-care with patients and their caregivers early in the disease trajectory, and establishing capacity for decision-making and advanced care planning, is also reviewed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.000
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 head0.091
GPT teacher head0.392
Teacher spread0.302 · 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.

Study designOther design
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

Citations11
Published2020
Admission routes1
Has abstractyes

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