Data Quality of Chemotherapy-Induced Nausea and Vomiting Documentation
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to characterize the completeness and concordance of the electronic health record (EHR) documentation of cancer symptoms among multidisciplinary health care professionals. METHODS: We examined the EHRs of children, adolescents, and young adults who received highly emetogenic chemotherapy and characterized the completeness and concordance of chemotherapy-induced nausea and vomiting (CINV) documentation by clinician type and by the International Classification of Diseases 10th Revision (ICD-10) coding choice. RESULTS: The EHRs of 127 patients, comprising 870 patient notes, were abstracted and reviewed. A CINV assessment was documented by prescribers in 75% of patients, and by nurses in 58% of patients. Of the 60 encounters where both prescribers and nurses documented, 72% agreed on the presence/absence of CINV. CONCLUSION: Most patients receiving highly emetogenic chemotherapy had a documented assessment of CINV; however, many had incomplete or discordant documentation of CINV from different providers by role, implying the importance of incorporating pragmatic knowledge of EHR documentation patterns among multidisciplinary health professionals for EHR phenotyping and clinical decision support systems directed toward cancer-related symptom management.
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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 | 0.047 | 0.223 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".