Nausea and Vomiting during the First 3 Intercycle Periods in Chemo-naive Cancer Patients Receiving Moderately/Highly Emetogenic Therapy
Bibliographic record
Abstract
AIMS AND BACKGROUND: There is a paucity of data regarding the incidence, intensity, and treatment of nausea and vomiting during the intercycle periods of chemotherapy (CHT). The aims of the study were to assess the incidence and intensity of intercycle nausea and vomiting, to assess the use of rescue antiemetic medications, and to define the more uncomfortable symptom between nausea and vomiting. METHODS: In a prospective study, 108 chemotherapy-naive patients treated with highly or moderately emetogenic CHT for different primary cancers were enrolled. All patients filled out the Edmonton Symptom Assessment System tool before the first cycle of CHT (T0) and on 14-16 days thereafter for the first 3 cycles of CHT (i.e., T1, T2, T3). RESULTS: Sixty-seven patients completed the study. During CHT administration, all patients received antiemetics according to international guidelines. During the intercycle periods, nausea was reported in 6.0% of patients at T0, 10.5% at T1, and 26.9% at T2 and T3, respectively. The intensity of nausea was mild for 6.0%, 21%, and 18% of patients at T1, T2, and T3, respectively; moderate for 1.5%, 3.0%, and 6.0% at T1 to T3; and severe in only 3.0% of patients at any time. Vomiting was present in 1.5% and 10.5% of patients at T2 and T3. Rescue antiemetic medication was required for 41.8% at T1, 53% at T2, and 47.8% at T3. At the end of the study, 70.1% of patients described nausea as the more uncomfortable symptom compared to vomiting. CONCLUSIONS: Nausea has a higher burden of impact over vomiting and should be assessed and treated separately throughout multiple cycles of CHT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".