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Record W3134553743 · doi:10.1002/pbc.28947

Prevention and treatment of anticipatory chemotherapy‐induced nausea and vomiting in pediatric cancer patients and hematopoietic stem cell recipients: Clinical practice guideline update

2021· article· en· W3134553743 on OpenAlexafffund
Priya Patel, Paula D. Robinson, Katie A. Devine, Karyn Positano, Marie Cohen, Paul Gibson, Mark T. Holdsworth, Bob Phillips, Daniela Spinelli, Jennifer Thackray, Marianne D. van de Wetering, Deborah Woods, Sandra Cabral, Lillian Sung, L. Lee Dupuis

Bibliographic record

VenuePediatric Blood & Cancer · 2021
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of TorontoPediatric Oncology GroupSickKids FoundationMcMaster Children's HospitalHospital for Sick Children
FundersNational Institute for Health and Care ResearchPediatric Oncology Group of Ontario
KeywordsMedicineChemotherapy-induced nausea and vomitingNauseaGuidelineIntensive care medicineVomitingPsychological interventionOncologyInternal medicineAntiemeticPsychiatry

Abstract

fetched live from OpenAlex

This 2021 clinical practice guideline update provides recommendations for preventing anticipatory chemotherapy-induced nausea and vomiting (CINV) in pediatric patients. Recommendations are based on systematic reviews that identified (1) if a history of acute or delayed CINV is a risk factor for anticipatory CINV, and (2) interventions for anticipatory CINV prevention and treatment. A strong recommendation to optimize acute and delayed CINV control in order to prevent anticipatory CINV is made. Conditional recommendations are made for hypnosis, systematic desensitization, relaxation techniques, and lorazepam for the secondary prevention of anticipatory CINV. No recommendation for the treatment of anticipatory CINV can be made.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.036
GPT teacher head0.366
Teacher spread0.330 · 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 designObservational
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

Citations29
Published2021
Admission routes2
Has abstractyes

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