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Record W3123200380 · doi:10.21037/apm-2019-pcno-12

Palliative care in neuro-oncology

2021· article· en· W3123200380 on OpenAlexaff
Jerome Graber, Hany Soliman

Bibliographic record

VenueAnnals of Palliative Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicinePalliative careIntensive care medicineOncologyInternal medicineNursing

Abstract

fetched live from OpenAlex

We are so pleased to introduce this series of the Annals of Palliative Medicine about Palliative Care in Neuro-Oncology.Patients with primary or metastatic tumors affecting the nervous system present a tremendous and sometimes overwhelming array of challenges in all aspects of palliative care.Patients and their caregivers face symptoms and side effects from neurosurgical, radiation, chemotherapy and neurological issues, requiring a coordinated effort from all these disciplines, and specialist centers are mainly concentrated in large urban areas, leaving a huge population of patients deprived of multidisciplinary specialty care.Fortunately, palliative care providers are uniquely enabled and prepared to address the wide spectrum of issues that may arise across the time from initial diagnosis through treatment and its complications, neurologic decline and bereavement.This series focuses on a broad range of topics in neuro-oncology including oncological management, death and dignity, mental health, care-giver bereavement, and religious/spiritual concerns.We are so grateful for our many colleagues across disciplines who assist our patients (and ourselves) in providing this care.Many of them are represented in this series (and we only wish there was room for more!): like the articles from neurologists Dr. Sharma and Drs.Ironside and Perry on prognostication and management of gliomas; radiation oncologists Drs Nguyen and Soliman, Drs.Chow, Drs.Lo and Tseng, and Drs.Vellayappan and Sahgal on the benefits and consequences of radiation therapy; and the psychologists, psychiatrists, social workers and chaplains like Drs.Gibson, Korman, Ellis, Fitchett, Isenberg-Grzeda, Morris, Nurse Practitioner Claire Moroney, and LCSW Sofie who guide our patients and families to manage distress and find meaning and dignity when facing and surviving their disease.We trust these articles, from the many perspectives represented, will help guide our patients and families, and ourselves, as we all work and hope together to improve care for neuro-oncology patients and their caregivers.

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)0.002
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0040.013
Insufficient payload (model declined to judge)0.0070.002

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 head0.283
GPT teacher head0.506
Teacher spread0.223 · 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
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

Citations1
Published2021
Admission routes1
Has abstractyes

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