Chemotherapy-induced nausea and vomiting from oral chemotherapy for childhood acute lymphoblastic leukaemia: feasibility study
Bibliographic record
Abstract
OBJECTIVE: To evaluate the feasibility of a large prospective trial aimed at improving chemotherapy-induced nausea and vomiting (CINV) control in paediatric patients undergoing oral chemotherapy during acute lymphoblastic leukaemia (ALL) maintenance therapy. METHODS: English-speaking children, 4.0-17.99 years old and undergoing ALL maintenance treatment with an English-speaking guardian, were eligible to participate in this observational, serial, cross-sectional feasibility study. Data were collected from participants over one to three 7-day periods during months 2-3, 5-6 and 11-12 of ALL maintenance treatment. A future trial was considered feasible if the mean time to enrol 10 patients in each of three data collection periods was ≤1 year with ≥80% of patients returning evaluable data. CINV control was described as a secondary endpoint. RESULTS: Twenty-nine of 31 consenting patients (median age: 6.5 years, IQR: 5.1-9.2) completed the study: 10 in months 2-3, 10 in months 5-6 and 9 in months 11-12. The total time to recruit 29 patients was 1.2 years. In each of the three data collections periods, 72% of the patients provided evaluable data. Complete CINV control was reported in 6/21 (29%) evaluable study periods. CONCLUSIONS: A future trial to evaluate interventions to improve CINV control in patients with ALL undergoing oral maintenance chemotherapy as designed in this study is not feasible. An electronic data capture method and deferring patient recruitment until the mid-maintenance to late-maintenance phase should be considered in the design of a future trial.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".