The role of comparative effectiveness research in transfusion medicine clinical trials: proceedings of a National Heart, Lung, and Blood Institute workshop
Bibliographic record
Abstract
Comparative effectiveness research (CER) is the study of existing treatments or ways to deliver health care to determine what intervention works best under specific circumstances. CER evaluates evidence from existing studies or generates new evidence, in different populations and under specific conditions in which the treatments are actually used. CER does not embrace one research design over another but compares treatments and variations in practice using methods that are most likely to yield widely generalizable results that are directly relevant to clinical practice. Treatments used in transfusion medicine (TM) are among the most widely used in clinical practice, but are among the least well studied. High-quality evidence is lacking for most transfusion practices, with research efforts hampered by regulatory restrictions and ethical barriers. To begin addressing these issues, the National Heart, Lung, and Blood Institute convened a workshop in June 2011 to address the potential role of CER in the generation of high-quality evidence for TM decision making. Workshop goals were to: 1) evaluate the current landscape of clinical research, 2) review the potential application of CER methods to clinical research, 3) assess potential barriers to the use of CER methodology, 4) determine whether pilot or vanguard studies can be used to facilitate planning of future CER research, and 5) consider the need for and delivery of training in CER methods for researchers.
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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 | 0.638 | 0.582 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.024 | 0.020 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.029 | 0.044 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".