Use and Misuse of Waterfall Plots
Bibliographic record
Abstract
BACKGROUND: "Waterfall plots" are used to describe changes in tumor size observed in clinical studies. Here we assess criteria for generation of waterfall plots and the impact of measurement error in generating them. METHODS: We reviewed published waterfall plots to investigate variability in criteria used to define them. We then compared waterfall plots generated by different observers for 24 patients enrolled in a completed phase I study of solid tumors with available computed tomography (CT) scans. Tumor measurements were made independently from CT scans according to Response Evaluation Criteria in Solid Tumors 1.1 by four board-certified radiologists and four medical oncologists. Interobserver variability was quantified and compared with reference measurements reported for the phase 1 study. All statistical tests were two-sided. RESULTS: There was substantial variability in criteria used to generate published waterfall plots. In the internal study, the results were statistically significantly different between all eight readers (P = .01, variance = 197.1, SD = 14.0) and between the oncologists (P = .01, variance = 319.0, SD = 17.9), but not between the radiologists (P = .68, variance = 70.8, SD = 8.4). Different observers classified one to five patients as having a partial response and 12-19 patients as having stable disease. Similar variability in categorization of response was observed when these error rates were applied to published waterfall plots. CONCLUSION: Waterfall plots are subject to substantial variability in criteria used to define them and are influenced by measurement errors; they should be generated by trained radiologists. Caution should be exercised when interpreting results of waterfall plots in the context of clinical trials.
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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 | 0.157 | 0.615 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".