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Record W2018024311 · doi:10.1159/000227647

Quality of Life Studies in Chemotherapy-Induced Emesis

2009· article· en· W2018024311 on OpenAlexaff
David Osoba, Benny Zee, David Warr, Leonard Kaizer, Jean Latreille, Joseph L. Pater

Bibliographic record

VenueOncology · 2009
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsUniversité de MontréalHôtel-Dieu de MontréalCredit Valley HospitalUniversity of TorontoOntario Institute for Cancer ResearchBC Cancer Agency
Fundersnot available
KeywordsAnorexiaVomitingChemotherapyQuality of life (healthcare)MedicineNauseaInternal medicineAnesthesiaNursing

Abstract

fetched live from OpenAlex

Health-related quality of life (HQL) was assessed before and after either moderately or highly emetogenic chemotherapy. When the pretreatment HQL in patients who did not vomit after chemotherapy (n = 203) was compared to those who vomited (n = 230), it was found that patients who did not vomit had better physical, role, and social function scores as well as a better global quality of life score than did patients who had one or more episodes of vomiting. Furthermore, in patients who did not vomit, the pretreatment fatigue and anorexia scores were better than in patients who did vomit. Thus, pretreatment HQL scores appear to have value in predicting which patients will experience chemotherapy-induced emesis. In the week following chemotherapy, HQL change scores from prechemotherapy values for cognitive function, global quality of life, fatigue, anorexia, insomnia and dyspnea were significantly worse in the group experiencing emesis than in the group who remained completely free of emesis. There were no differences in physical, role, emotional and social function attributable to chemotherapy-induced vomiting.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.220
GPT teacher head0.491
Teacher spread0.271 · 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 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

Citations72
Published2009
Admission routes1
Has abstractyes

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