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Record W3003229900 · doi:10.5737/2368807630168

PERSPECTIVES INTERNATIONALES Efficacité du gingembre pour calmer les nausées et les vomissements causés par la chimiothérapie chez les patientes atteintes d’un cancer du sein

2020· article· fr· W3003229900 on OpenAlexvenueno aff
Dwi Fitriyanti, Reni Sulung

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2020
Typearticle
Languagefr
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

La chimiothérapie occasionne des effets secondaires non négligeables et parfois délétères pour les patients, notamment des nausées et des vomissements. Pour minimiser ces effets indésirables, de nombreux patients d'ici se tournent vers la phytothérapie, qui recommande dans ce cas la prise de gingembre. Le présent article a pour but d'évaluer l'efficacité du gingembre pour soulager les nausées et les vomissements des patientes atteintes d'un cancer du sein qui reçoivent de la chimiothérapie. Fondés sur une revue systématique, les résultats de l'étude indiquent que le gingembre serait efficace seulement pour apaiser les nausées - mais pas la fréquence des vomissements - des patientes sous chimiothérapie.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.030
GPT teacher head0.315
Teacher spread0.285 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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