Q-TWiST Analysis of Sacituzumab Govitecan vs. Chemotherapy in Previously Treated Patients with HR+/HER2− Metastatic Breast Cancer
Bibliographic record
Abstract
In TROPiCS-02, sacituzumab govitecan (SG) demonstrated significantly longer overall survival and progression-free survival with improved quality of life vs. chemotherapy treatment of physician’s choice (TPC) in patients with HR+/HER2− metastatic breast cancer (mBC). The safety profile was consistent with previous studies of SG. We assessed the benefit-–risk profile of SG vs. TPC by integrating patient preferences with clinical benefits using Quality-adjusted Time Without Symptoms of disease progression or Toxicity of treatment (Q-TWiST) analysis in this study population. Survival time was partitioned into three health states: TOX (grade ≥3 treatment-emergent adverse events [TEAEs] after randomization/before disease progression), REL (disease progression until death or end of follow-up), and TWiST (time without progression or grade ≥3 TEAEs). Health state utility weights were obtained from the published literature. The established threshold for clinically important Q-TWiST gain is 10%. SG demonstrated significantly improved Q-TWiST vs. TPC (mean 9.7 vs. 8.1 months; difference 1.6 months; 95% CI, 0.5–2.7; p = 0.0067), which increased with longer follow-up. Relative Q-TWiST improvement met the threshold for clinical importance at 10.8%. Time in TOX was numerically higher with SG than TPC, and the difference stabilized over time. Q-TWiST supports a positive benefit–risk profile for SG over TPC in patients with pretreated HR+/HER2− mBC.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".