Exploring the nausea experience among female patients with breast cancer; A pilot interview study
Bibliographic record
Abstract
INTRODUCTION: Nausea is a difficult symptom to report and measure in clinical trials. We conducted a pilot interview study to improve our understanding of the nausea experience. MATERIALS AND METHODS: Female patients with breast cancer that had experienced nausea during radiation therapy and/or chemotherapy underwent semi-structured interviews that focused on patient-defined and standard definitions, preferences for nausea grading scales, and nausea sub-features: intensity, location, timing/duration, character, associated symptoms, precipitating/alleviating factors, impact on quality of life. RESULTS: 10 patients were interviewed. Patients defined nausea more variably than vomiting and retching/dry heaving. An ordinal grading scale with a 0-10 intensity range was preferred over visual-analogue and qualitative scales. Patients had experienced different intensities of nausea and deemed reporting their worst, average and least intensities feasible. High-intensity episodes were deemed more problematic than low-intensity episodes regardless of their duration. The duration and character of nausea were difficult to describe. A range of associated symptoms, precipitating and alleviating factors were documented. Nausea had a detrimental impact on quality of life. CONCLUSIONS: Nausea has a range of subjective and objective features. Our pilot study provided valuable information that will inform the design of a planned larger survey study. Creating an operational clinical trial definition for nausea appears feasible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".