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Record W4384465867 · doi:10.5737/23688076333328

Soins infirmiers donnés pendant la chimiothérapie aux patients atteints de tumeurs gastro-intestinales

2023· article· fr· W4384465867 on OpenAlexvenueno aff
Donghui Dai, Jie Chen

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2023
Typearticle
Languagefr
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

Objectifs : La présente recherche menée auprès de patients atteints d’un cancer gastro-intestinal étudiait les effets escomptés des interventions infirmières sur a) le respect du traitement par les patients, b) leur satisfaction à l’égard des soins infirmiers et c) la santé physique, par exemple la fonction pulmonaire. Méthodologie : Tous les patients (60 personnes) ayant reçu de la chimiothérapie au service de médecine traditionnelle chinoise du First Affiliated Hospital of Soochow University, ont été répartis en deux groupes égaux. Aux patients du groupe 1, on a administré les soins usuels planifiés; les patients du groupe 2 ont bénéficié d’interventions infirmières fondées sur des données probantes. Résultats : Les résultats montrent que le respect du traitement était meilleur dans le groupe 2 que dans le groupe témoin (p=0,01). De plus, on a observé un degré de satisfaction plus élevé à l’égard des services infirmiers (p =0,01) et une amélioration de la fonction pulmonaire (p=0,01). Le respect du traitement a fait diminuer la proportion d’infections secondaires dans le groupe 2 (p =0,05). Conclusion : Les résultats montrent que des interventions infirmières de qualité permettent d’améliorer la fonction pulmonaire, de réduire le stress, de bonifier les plans de traitement et de réduire le nombre d’infections secondaires.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.109
GPT teacher head0.434
Teacher spread0.325 · 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
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
Published2023
Admission routes1
Has abstractyes

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