The Role of Clinical Guidelines for the Management of Chemotherapy-Induced Nausea and Vomiting in Children with Cancer
Bibliographic record
Abstract
Background: Nausea and vomiting are among the most important side-effects associated with chemotherapy in children with cancer, affecting the quality of their lives. Clinical guidelines for selecting antiemetics are effective in reducing acute chemotherapy-induced nausea and vomiting (CINV). Materials and Methods: The present quasi-experimental study compared the effectiveness of the Pediatric Oncology Group of Ontario (POGO) CINV guideline with that of conventional arbitrary therapies for CINV in 82 children aged 6 months to 16 years old. Out of 177 cycles of chemotherapy, in 101 cycles patients were treated according to POGO-CINV Guideline; in the other 76 cycles, patients were treated with arbitrary types and doses of antiemetics. Then, vomiting in the first 24 hours after chemotherapy in both groups was measured and compared. Results: In this study, 82 patients hospitalized in the Hematology Department of Dr. Sheikh Children’s Hospital were enrolled, of whom 48 patients (58.7%) were boys and 34 (41.3%) were girls. The mean age of patients was 6.24±4.47 years (6 months to 16 years). The results of the current study showed that using a protocol for the prevention of vomiting based on the patient’s age and the type of chemotherapy is superior to conventional management of CINV. Findings showed that the frequency of nausea and vomiting in the protocol group was significantly reduced in comparison with the control group (p˂0.005). Moreover, a reduction in the frequency of nausea and vomiting was quite significant in the sub-categories of the protocol group who had received high-risk or moderate-risk emetogenic drugs (p˂0.005). Conclusion: The results of the current study showed that using the POGO guideline, which takes into account the patient’s age and the type of chemotherapy, is more effective than arbitrary management of CINV, particularly in children.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".