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Record W3166422458 · doi:10.37506/ijfmt.v15i3.15511

Effect of Ginger Tea on Chemotherapy-Induced Nausea and Vomiting among Patients Attending the Oncology Teaching Hospital, Baghdad 2020

2021· article· en· W3166422458 on OpenAlexaff
Remal Adel Kadhim, Besmah Mohammed Ali, Maysaa Adel Kadhim, Samer Jassim Mohammed

Bibliographic record

VenueIndian Journal of Forensic Medicine & Toxicology · 2021
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsNauseaAntiemeticMedicineVomitingChemotherapy-induced nausea and vomitingRegimenChemotherapySignificant differenceAnesthesiaIntervention (counseling)Internal medicineNursing

Abstract

fetched live from OpenAlex

Background: Ginger has been widely used to relieve nausea and vomiting in several settings, one ofthem, patients receiving chemotherapy. This study was done to assess the effect of ginger in controlling thechemotherapy induced nausea and vomiting (CINV) among patients. Methods: An interventional (pre-post)study design was conducted in oncology teaching hospital in Baghdad for three months. Sixty participantswere randomly assigned into intervention group (30 participants received ginger tea (1.5 g/d) with routineantiemetic regimen for the first 5 days of the chemotherapy cycle) and control group (30 participants receivedonly routine antiemetic regimen). MASCC Antiemesis Tool (MAT) was used for assessment of CINV incancer patients before and after the use of ginger tea.Results: No significant difference was observed between the intervention and control groups in the acuteand delayed phases of CINV after intervention with ginger tea(p >0.05), but difference between the studygroups was found statistically significant (p <0.05)regarding the severity of nausea postchemotherapy.Conclusions: The addition of ginger tea to routine antiemetic regimen in patients receiving chemotherapyeffectively reduced the severity of nausea. However, there is no additional role for ginger in reducing theacute and delayed phases of CINV.

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)0.003
metaresearch head score (Gemma)0.003
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 score0.425
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.301
Teacher spread0.290 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

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