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Record W4285395866 · doi:10.1111/ecc.13642

Cancer‐related fatigue measures in palliative care: A psychometric systematic review

2022· review· en· W4285395866 on OpenAlexaboutno aff
Pedro Montagut‐Martínez, César Pérez‐Cruzado, Daniel Gutiérrez Sánchez

Bibliographic record

VenueEuropean Journal of Cancer Care · 2022
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCINAHLPalliative careChecklistMEDLINEGrading (engineering)Quality of life (healthcare)Family medicineNursingPsychological interventionPsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: In palliative care, the prevention and relief of fatigue are regarded as crucial goals in patients with cancer. METHODS: A systematic review was carried out according to the COnsensus-based Standards for the selection of health status Measurement INstruments methodology. Searches were conducted in Medline (through PubMed), Web of Science, Open Gray, Scielo, Cochrane, CINAHL and EMBASE. All instruments found in each study were assessed using the COnsensus-based Standards for the selection of health status Measurement INstruments checklist and Grading of Recommendations, Assessment, Development and Evaluation approach. RESULTS: A total of 5598 articles were identified in the different databases. In total, 57 studies describing 19 instruments were included in this study. The main properties evaluated were internal consistency, cross-cultural validity, hypotheses testing and responsiveness. All studies were evaluated with the Strobe scale with a score greater than 6 points. CONCLUSIONS: According to the quality methodological results, Edmonton Symptom Assessment System, Problems and Needs in Palliative Care Questionnaire, European Organisation for Research and Treatment of Cancer Quality of Life 15-item Questionnaire for Palliative Care and Palliative Care Quality of Life Instrument are the recommended instruments used for assessing cancer-related fatigue in palliative care. PROPESRO registration number: CRD42020206783.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.499
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.121
GPT teacher head0.397
Teacher spread0.276 · 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 designSystematic review
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

Citations6
Published2022
Admission routes1
Has abstractyes

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