Evaluating response to interventions for vasomotor symptoms in patients with breast cancer: A patient-centered approach.
Bibliographic record
Abstract
e24126 Background: Vasomotor symptoms (VMS), such as hot flashes, are a common reason for early discontinuation of endocrine therapy for patients (pts) with breast cancer (BC). The optimal intervention for VMS remains unknown. Using a novel symptom change analysis and regression trees, we examined whether clinically important change in VMS severity depends on baseline symptomatology. Methods: In this prospective study, pts with BC experiencing VMS chose either a lifestyle (LS), or non-LS (i.e. complementary and alternative therapies [CAM], prescription medications [PM], or endocrine therapy modification [ETM]) intervention. The primary outcome was change in symptom severity using the 10-point Hot Flush Rating Scale (HFRS). Patients declining interventions were included as controls. At the end of the 6-week intervention, participants rated the effectiveness of their chosen intervention on a five-point Likert scale. A logistic sigmoid function and Bayesian optimization were used to weight the change in VMS score based on baseline VMS severity. A regression tree was trained to predict which intervention resulted in the greatest improvement in VMS at 6 weeks. Results: 100 baseline and follow-up questionnaires from 85 pts were included in an intention to treat analysis. The median baseline HFRS was 5.0 (IQR 3.33 ,7.00). Selected interventions included LS (27%), CAM (25%), PM (11%) and ETM (8%), with 29% declining interventions. After removal of missing data, 59 individuals provided responses for both the HFRS score and the effectiveness score. Higher baseline HFRS scores had greater impact on symptom change scores. The largest difference in VMS improvement was noted between the non-LS (median symptom change –0.82, IQR –2.00, –0.01) and LS/control groups (median symptom change – 0.38, IQR –1.46, 0.00) (Table 1). Of non-LS interventions, CAM had the greatest improvement in VMS severity. Pre-menopausal individuals who pursued LS interventions had the least improvement in VMS severity. Conclusions: Interventions for VMS were most impactful among pts with greater baseline symptoms. While LS interventions were the most commonly selected intervention, the resulting improvement in symptoms at 6 weeks was negligible, and alternative strategies should be encouraged. Future studies integrating pt preferences and accounting for baseline symptom severity are needed. [Table: see text]
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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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".