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Record W2797474306 · doi:10.5737/23688076282139145

PERSPECTIVES INTERNATIONALES Efficacité de l’acupression sur les symptômes de nausées et de vomissements chez les patients en chimiothérapie

2018· article· fr· W2797474306 on OpenAlexvenueno aff
Anju Byju, Sheela Pavithran, Regina Antony

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2018
Typearticle
Languagefr
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

L’étude, d’une durée de six semaines, a été menée selon un mode d’échantillonnage dirigé pour explorer les effets de l’acupression sur les nausées et les vomissements de 40 patients cancéreux recevant de la chimiothérapie. Les données ont été recueillies à partir d’entrevues semi-structurées, d’un questionnaire semi-structuré et de l’indice de Rhodes des nausées, vomissements et haut-le-cœur. Conçue selon un devis quasi-expérimental, l’étude employait un groupe témoin avec post-test seulement. L’analyse des données a fait intervenir des statistiques descriptives et inférentielles de la fréquence, du pourcentage, du khi carré et des tests t pour échantillons indépendants. Les résultats montrent des symptômes de nausées et de vomissements légers (65 %) à modérés (35 %) pour les sujets du groupe expérimental, tandis que les sujets du groupe témoin ont, de leur côté, souffert de nausées et de vomissements modérés (35 %) à intenses (65 %), avec une valeur t (38) = 2,693, 8,270, 8,401 respectivement pour les jours 1, 2 et 3; p < 0,05. Selon ces résultats, l’acupression serait donc efficace pour réduire les nausées et les vomissements chez les patients recevant de la 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)0.007
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.338
Teacher spread0.316 · 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
Published2018
Admission routes1
Has abstractyes

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