Mitigating and Quantifying Cherry-Picking in Acute Stroke Trials
Bibliographic record
Abstract
Consecutive enrollment of eligible patients is fundamental to the internal and external validities of randomized controlled trials. The generalizability of trial results is greatly undermined when enrolled patients are not representative of the broader target population, which is especially likely if a large proportion of otherwise-eligible individuals receive treatment outside the trial. In this article, we discuss the problem that such selective recruitment or cherry-picking of patients poses to clinical trials. We explore factors contributing to such cherry-picking, such as the What's In It For Us problem for subinvestigators, and discuss strategies to identify when cherry-picking is going on in a trial. We also critically examine various strategies to mitigate cherry-picking. Moreover, we propose a method to quantify cherry-picking within an acute stroke trial while distinguishing it from simple underrecruitment. It is only when we seek to consistently quantify the problem of cherry-picking, that we will make meaningful strides toward resolving this issue and further strengthening the validity of our randomized controlled trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".