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Record W1578613240 · doi:10.1002/pbc.25063

Guideline for the prevention and treatment of anticipatory nausea and vomiting due to chemotherapy in pediatric cancer patients

2014· review· en· W1578613240 on OpenAlexafffund
L. Lee Dupuis, Paula D. Robinson, Sabrina Boodhan, Mark T. Holdsworth, Carol Portwine, Paul Gibson, Cathy Maan, Nancy Stefin, Lillian Sung

Bibliographic record

VenuePediatric Blood & Cancer · 2014
Typereview
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsLondon Health Sciences CentreMcMaster UniversityUniversity of TorontoSickKids FoundationPediatric Oncology GroupInstitute for Clinical Evaluative SciencesHospital for Sick Children
FundersCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsMedicineGuidelineNauseaVomitingChemotherapy-induced nausea and vomitingIntensive care medicinePsychological interventionChemotherapyAntiemeticInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

This guideline provides an approach to the prevention and treatment of anticipatory chemotherapy-induced nausea and vomiting (CINV) in children. It was developed by an international, inter-professional panel using AGREE II methods and is based on systematic literature reviews. Evidence-based recommendations for pharmacological and non-pharmacological interventions to prevent and treat anticipatory CINV in children receiving antineoplastic agents are provided. Gaps in the evidence used to support the recommendations are identified. The contribution of this guideline to anticipatory CINV control in children requires prospective evaluation.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.049
GPT teacher head0.389
Teacher spread0.339 · 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 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

Citations82
Published2014
Admission routes2
Has abstractyes

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