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Record W4318754863 · doi:10.1089/jpm.2022.0598

Top Ten Tips Palliative Care Clinicians Should Know About Cachexia

2023· article· en· W4318754863 on OpenAlexaff
David Blum, Ola Magne Vagnildhaug, Guro B. Stene, Matthew Maddocks, Jonas Sørensen, Barry Laird, Carla M. Prado, Tora S. Solheim, Jann Arends, Jane B. Hopkinson, Christopher A. Jones, Mathias Schlögl

Bibliographic record

VenueJournal of Palliative Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
FundersNational Institute for Health and Care Research
KeywordsCachexiaMedicineBiopsychosocial modelPalliative careIntensive care medicineQuality of life (healthcare)DiseaseCancerNursingInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Cachexia is a multifactorial syndrome that is common in cancer and chronic disease. It is often underdiagnosed and therefore goes untreated or undertreated. Cachexia causes suffering across biopsychosocial domains and affects patients and their loved ones. In this article, a group of clinicians and researchers across cancer care, nutrition, and exercise offers tips about assessment, classification, and management of cachexia, with attention to its stage. The required multimodal management of cachexia mirrors well the interprofessional collaboration that is the mainstay of interdisciplinary palliative care and attention to screening, diagnosis, and management of cachexia is critical to maximize patients' quality of life.

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)0.027
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.048
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0040.007
Open science0.0010.003
Research integrity0.0090.011
Insufficient payload (model declined to judge)0.0480.023

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 head0.179
GPT teacher head0.474
Teacher spread0.295 · 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
GenreCommentary

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
Published2023
Admission routes1
Has abstractyes

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