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Record W4376309417 · doi:10.1002/pbc.30395

Treatment of breakthrough and prevention of refractory chemotherapy‐induced nausea and vomiting in pediatric cancer patients: Clinical practice guideline update

2023· article· en· W4376309417 on OpenAlexaff
Priya Patel, Paula D. Robinson, Christina Baggott, Katie A. Devine, Paul Gibson, Gregory M.T. Guilcher, Mark T. Holdsworth, Eloise Neumann, Andrea D. Orsey, Daniela Spinelli, Jennifer Thackray, Marianne D. van de Wetering, Sandra Cabral, Lillian Sung, L. Lee Dupuis

Bibliographic record

VenuePediatric Blood & Cancer · 2023
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsAlberta Children's HospitalInstitute for Clinical Evaluative SciencesUniversity of CalgaryMcMaster Children's HospitalHospital for Sick ChildrenUniversity of TorontoSickKids FoundationPediatric Oncology Group
Fundersnot available
KeywordsMedicineAntiemeticChemotherapy-induced nausea and vomitingGuidelineNauseaRefractory (planetary science)VomitingIntensive care medicineClinical trialRandomized controlled trialClinical PracticeChemotherapyInternal medicinePhysical therapy

Abstract

fetched live from OpenAlex

This clinical practice guideline update provides recommendations for treating breakthrough chemotherapy-induced nausea and vomiting (CINV) and preventing refractory CINV in pediatric patients. Two systematic reviews of randomized controlled trials in adult and pediatric patients informed the recommendations. In patients with breakthrough CINV, escalation of antiemetic agents to those recommended for chemotherapy of the next higher level of emetogenic risk is strongly recommended. A similar recommendation to escalate therapy is made to prevent refractory CINV in patients who did not experience complete breakthrough CINV control and are receiving minimally or low emetogenic chemotherapy. A strong recommendation to use antiemetic agents that controlled breakthrough CINV for the prevention of refractory CINV is also 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 score0.150
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.037
GPT teacher head0.392
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 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

Citations15
Published2023
Admission routes1
Has abstractyes

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