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Record W4317934697 · doi:10.3390/curroncol30020117

Evaluation of Current Antiemetic Therapy Response in Patients Undergoing MEC or HEC Regimens in Portugal

2023· article· en· W4317934697 on OpenAlexvenueno aff
António Araújo, N. Tavares, Joana Mendonça, Bárbara Parente, Andreia Capela, Fernando Barata, Ana Macedo

Bibliographic record

VenueCurrent Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicNausea and vomiting management
Canadian institutionsnot available
FundersVifor Pharma
KeywordsAntiemeticMedicineNauseaChemotherapy-induced nausea and vomitingVomitingChemotherapyDexamethasoneQuality of life (healthcare)Intensive care medicineAnesthesiaPopulationCancerInternal medicineOncologyNursing

Abstract

fetched live from OpenAlex

Chemotherapy-induced nausea and vomiting (CINV) negatively impact cancer patients' quality of life and treatment outcomes. This study evaluated the achievement of complete response to CINV prophylaxis during the first five days after chemotherapy in adult outpatient cancer clinics with solid malignant tumours receiving Moderate or Highly Emetogenic Chemotherapy (MEC or HEC) in Portugal. During the study, patients completed three evaluations, and nausea severity and CINV impact on patients' daily life was assessed. A complete response (no emetic episodes, no use of rescue antiemetic medication, and no more than mild nausea) was observed in 72% of the cycles (N = 161) throughout the five days after chemotherapy. Amongst the patient population, 25% classified their CINV episodes as severe. Though more than half of the patients achieved a complete response, suggesting that a therapeutic effort is being made to minimise this side effect, the overall scenario is barely optimistic. Significantly, new CINV-control measures in MEC/HEC patients should be adopted, specifically avoiding the single use of dexamethasone and 5-HT3 and raising awareness of using NK1-RAs. Thus, it is critical to improve CINV prophylactic treatment and implement practical international antiemetic guidelines in Portuguese clinical practice, envisaging the improvement of supportive care for cancer patients.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.450
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.338
GPT teacher head0.497
Teacher spread0.159 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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