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Record W2171023745 · doi:10.1586/14737175.2014.890890

Seizure management in patients with gliomas

2014· review· en· W2171023745 on OpenAlexaff
Catherine Maurice, Warren Mason

Bibliographic record

VenueExpert Review of Neurotherapeutics · 2014
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineEpilepsyQuality of life (healthcare)Radiation therapyGliomaChemotherapyIntensive care medicineSurgeryPsychiatry

Abstract

fetched live from OpenAlex

Seizures represent a major cause of morbidity in patients diagnosed with brain tumors. Seizures in patients with gliomas are disruptive, impact on quality of life; autonomy; the capacity to operate motor vehicles and opportunities for work. The management of seizures in patients with brain tumors is complex and ideally managed in a multidisciplinary fashion. In addition to antiepileptic drugs, surgery, chemotherapy and radiotherapy have potential roles in the management of a glioma patient with intractable epilepsy. The successful management of seizures in patients with brain tumors is possible, it provides considerable benefits in terms of quality of life and should remain a central goal in patient management.

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 score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.041
GPT teacher head0.395
Teacher spread0.353 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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