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Record W2402814597 · doi:10.12968/bjon.2016.25.10.s12

Translational research and symptom management in oncology nursing

2016· review· en· W2402814597 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBritish Journal of Nursing · 2016
Typereview
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsTranslational researchMedicineContext (archaeology)Nursing researchOncology nursingNursingMEDLINENursing practiceAlternative medicineNursing managementNurse educationPathology

Abstract

fetched live from OpenAlex

In recent years, translational research (TR) has become a new approach for bridging basic research and clinical practice. This article examines studies in which the authors used TR to learn more about the underlying causes of selected symptoms, and to discuss these results in the context of cancer nursing and symptom management. A literature review was undertaken, plus critical analysis of the authors. TR conducted by cancer nursing scholars has been relatively limited in the past, but is becoming more common as nurses complete additional academic work in the basic sciences and develop research teams with colleagues of those areas of knowledge. The goal in these studies is to show how a set of variables explains differential interventional effects. The availability of TR provides new evidence for the management of symptoms experienced by individuals with cancer, which could lead to improvements in the care of cancer patients across the world.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.169
GPT teacher head0.494
Teacher spread0.325 · 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