Prospective Validation of Risk Prediction Indexes for Acute and Delayed Chemotherapy-Induced Nausea and Vomiting
Bibliographic record
Abstract
BACKGROUND: Despite the use of standardized anti-emetic guidelines, up to 20% of cancer patients suffer from moderate-to-severe chemotherapy-induced nausea and vomiting (cinv)-that is, grade 2 or greater according to the U.S. National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0. We previously developed cycle-based prediction models and associated scoring systems for acute and delayed cinv. As part of the validation process, we prospectively evaluated the ability of the scoring systems to accurately identify patients deemed to be high risk for grade 2 or greater cinv. METHODS: Patients who were receiving any chemotherapy for solid tumours and who consented to participate were provided with symptom diaries. Compliance to the diaries was enhanced by 24-hour and 5-day telephone callbacks after chemotherapy in every cycle. All patients received anti-emetic prophylaxis as prescribed by the treating physician. Before each cycle of chemotherapy, the acute and delayed cinv scoring systems were used to stratify patients into low- and high-risk groups. Logistic regression modelling was then applied to compare the risk for grade 2 or greater cinv between patients considered to be at high and at low risk. The external validity of each system was also assessed using an area under the receiver operating characteristic curve (auroc) analysis. RESULTS: We collected cinv outcomes data from 95 patients during 181 cycles of chemotherapy. The incidence of grade 2 or greater acute and delayed cinv was 17.7% and 18.2% respectively. As previously identified, major predictors for grade 2 or greater cinv included younger patient age, platinum- or anthracycline-based chemotherapy, low alcohol consumption, earlier cycles of chemotherapy, previous history of morning sickness, and prior emetic episodes after chemotherapy. The acute and delayed scoring systems both had good predictive accuracy when applied to the external validation sample (acute-auroc: 0.69; 95% confidence interval: 0.59 to 0.79; delayed-auroc: 0.70; 95% confidence interval: 0.60 to 0.80). Patients identified by the scoring systems to be at high risk were 2.8 (p = 0.025) and 3.1 (p = 0.001) times more likely to develop grade 2 or greater acute and delayed cinv. CONCLUSIONS: The present study demonstrates that our scoring systems are able to accurately identify patients at high risk for acute and delayed cinv. Application and planned continued refinement of the scoring systems will be an important means of patient-specific risk assessment that will allow for optimization of anti-emetic therapy.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".