The impact of radiological assessment schedules on progression-free survival in metastatic breast cancer: A systemic review and meta-analysis.
Bibliographic record
Abstract
1086 Background: The impact of the interval of radiological assessment on the magnitude of benefit observed in randomized trials (RCTs) in metastatic breast cancer is undefined. Methods: All RCTs investigating anti-neoplastic drugs for metastatic breast cancer published between 2006 and 2019 were identified. Intervals for restaging were categorized as short ( < 9 weeks) or long (≥9 weeks). Hazard ratios (HRs) and 95% confidence intervals for progression-free survival (PFS) and overall-survival (OS) were pooled in a meta-analysis and compared between trials employing short and long restaging intervals assessed as subgroup analyses. Analyses were repeated for pre-specified subgroups according to disease subtype, drug type, whether experimental therapy was added to or replaced standard treatment and whether HR for PFS was < 1 or ≥1. Results: Eighty-nine studies comprising 95 comparisons and 44,901 patients were included. The magnitude of PFS benefit was non-significantly larger in trials which employed short compared to long restaging intervals (HR 0.79 vs. 0.86, p = 0.15). Short restaging interval was associated with significantly higher magnitude of effect on PFS in pre-specified subgroups including non-first line studies (HR 0.78 vs. 0.92, p = 0.04), studies with drugs replacing standard treatment (HR 0.86 vs. 1.04, p = 0.02) and studies performed exclusively in human epidermal growth factor receptor 2 (HER2) positive disease (HR 0.72 vs. 0.90, p = 0.02). Restaging interval was not associated with OS for all included studies (HR 0.92 vs. 0.93, p = 0.66) or for any of the pre-specified subgroups. Conclusions: Shorter restaging intervals are associated with a higher magnitude of effect of PFS, but not OS. Awareness of the impact of the restaging interval on quantification of intermediate endpoints such as PFS is important for the design and interpretation of RCTs.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.036 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".