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Record W4304758361 · doi:10.1002/pbc.30001

Prevention of acute and delayed chemotherapy‐induced nausea and vomiting in pediatric cancer patients: A clinical practice guideline

2022· article· en· W4304758361 on OpenAlexaff
Priya Patel, Paula D. Robinson, Marie Cohen, Katie A. Devine, Paul Gibson, Mark T. Holdsworth, Eloise Neumann, Andrea D. Orsey, Daniela Spinelli, Jennifer Thackray, Marianne D. van de Wetering, Deborah Woods, Sandra Cabral, Lillian Sung, L. Lee Dupuis

Bibliographic record

VenuePediatric Blood & Cancer · 2022
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 Research
KeywordsMedicineChemotherapy-induced nausea and vomitingAntiemeticNauseaGuidelineVomitingDosingIntensive care medicineChemotherapyClinical trialDexamethasoneAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

This clinical practice guideline provides recommendations for preventing acute and delayed phase chemotherapy-induced nausea and vomiting (CINV) in pediatric patients. The recommendations are based on two systematic reviews of randomized controlled trials evaluating interventions to prevent (1) acute phase CINV and (2) delayed phase CINV. Recommendations for acute phase and delayed phase CINV prophylaxis are made for patients receiving chemotherapy of varying emetogenicity, as well as for patients not able to receive dexamethasone or a neurokinin-1 receptor antagonist. Evidence gaps, including antiemetic safety and optimal dosing, were identified.

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)0.006
metaresearch head score (Gemma)0.023
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0040.002
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0060.004

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 head0.023
GPT teacher head0.371
Teacher spread0.348 · 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
GenreMethods

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

Citations50
Published2022
Admission routes1
Has abstractyes

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