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Record W1974599133 · doi:10.1159/000227062

Managing Treatment–Induced Emesis: A Nursing Perspective

2009· article· en· W1974599133 on OpenAlexaff
Marg Fitch

Bibliographic record

VenueOncology · 2009
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsThunder Bay Regional Health Sciences Centre
Fundersnot available
KeywordsNauseaVomitingMedicineIntensive care medicineRadiation therapyQuality of life (healthcare)ChemotherapyPerspective (graphical)DiseaseCancerNursingSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Patients with cancer undergoing treatment with chemotherapy or radiation therapy may experience a range of treatment-related side effects. One of the most common and most distressing side effects is treatment-induced emesis. The severity of the symptom is great enough to cause some patients to refuse treatments, delay appointments or discontinue therapy entirely. Experiencing treatment-induced nausea and vomiting can create a spectrum of issues for patients and their families and seriously influence their quality of life. Managing nausea and vomiting induced by cancer therapy is of critical importance. A team approach, inclusive of the patient, can be most effective. Nurses play a pivotal role in assessing patterns of nausea and vomiting and the usefulness of anti-emetic therapy, evaluating and updating treatment/care plans and helping the patient and family to cope with the disease and its treatment.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.050
GPT teacher head0.405
Teacher spread0.355 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations4
Published2009
Admission routes1
Has abstractyes

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