Which design for which question? An exploration toward a translation table for comparative effectiveness research
Bibliographic record
Abstract
In this paper, we explore the relative value that different methods offer in answering some stereotypical comparative effectiveness research questions with the goal of informing development of a 'translation table'--a selection tool for choosing appropriate methods for specific comparative effectiveness research questions. This paper was written as a parallel effort to Greenfield and Kaplan (also in this volume) to support the endeavor described in the manuscript by Tunis et al. (also in this volume). Originally based on four cases, the current article has been shortened to two cases for the current discussion. These cases represent research priorities proposed to orient the work of the Patient-Centered Outcomes Research Institute, comparative clinical effectiveness and comparative health services.
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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 | 0.495 | 0.543 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.017 | 0.030 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".