Bibliographic record
Abstract
In this EBM Hub edition, we use the interesting, prospective, randomized, double-blinded study of Min et al1 from this current issue of Aesthetic Surgery Journal as a springboard to address a key methodological issue that is important in the proper execution and reporting of a randomized, controlled trial: the method of randomization . Randomization is among our most powerful protective mechanisms for removing bias from a study. A well-performed randomization makes it more likely that the study conclusions will be valid.2 Randomization works by reducing the chance that our pre-knowledge of certain factors will influence how the study is performed, specifically that our pre-knowledge of patient factors does not influence which patients get which treatments, or that we might perform the treatments differently based on such knowledge. Investigators, reviewers, and journal editors put a lot of emphasis on the method of randomization, and as a reader, so should you. Why? How much difference can it make? With trials, a small error at one point (eg, randomization) multiplies another error at another point, and so on, therefore, several small errors can lead to a totally wrong, backwards result. (Two other tools, blinding and allocation concealment, also prevent pre-knowledge from influencing how trials are performed and assessed. Click on the following link to learn more about how blinding and allocation concealment work alongside randomization: http://youtu.be/znBuDyMjhTM) When …
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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 | 0.324 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.023 |
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; both teacher heads agree on what is shown here.
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".