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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 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)0.003
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0070.004

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations29
Published2021
Admission routes2
Has abstractyes

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