Integration of a nausea and vomiting assessment tool into antineoplastic management of pediatric oncology patients
Bibliographic record
Abstract
Background Antineoplastic-induced nausea and vomiting (AINV) is a treatment-related issue that can have significant negative influences on the cancer patient’s quality of life. Assessment of nausea is challenging in children as few studies include the perception of nausea as an outcome, and the severity is rarely evaluated with the use of a validated instrument. We describe our experience of integrating an AINV tool into patient care at the Alberta Children’s Hospital. Procedure: The Pediatric Nausea Assessment Tool (PeNAT) was adapted to create a standardized tool that could be used by the clinical pharmacists for AINV assessment. From February to August 2017, 74 patients receiving 217 cycles of highly or moderately emetogenic chemotherapy (HEC or MEC) were eligible to use the AINV tool. Patients that completed the AINV tool were contacted to complete a satisfaction survey. Results AINV tool uptake was low: 47 (22%) eligible chemotherapy cycles utilized the tool (24 (32%) and 23 (16%) cycles of HEC and MEC, respectively, ( p < 0.01)). Ifosfamide-containing cycles received the highest nausea ratings, with nausea severity correlated with agent emetogenicity. Mean nausea rating was 2.07 versus 1.76 for patients receiving HEC or MEC, respectively. Clinical pharmacists performed 1.24 AINV interventions per day. Patient satisfaction with AINV care overall was high; however, 51% of patients indicated that the tool led to no changes in nausea symptoms. Conclusions AINV tool uptake was low with limited value in improving outcomes. Incorporation of nausea assessment into the electronic health record and potential use of a mobile application may improve uptake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".