Critical Care: A Special Issue of the Blue Journal
Bibliographic record
Abstract
Welcome to the March 1, 2024, issue of the Journal, a special issue dedicated to critical care.Readers will find a broad array of topics, research, and debate in the following pages, from translational to clinical to emerging perspectives on the design and interpretation of studies in our field. Clinical ResearchAccordingly, bringing all these topics together, the special edition includes a special perspective on the international landscape of adaptive trials in critical care (pp.491-496; 1).With authors from five major adaptive trial consortiums that are, or will soon be, under way (i.e., the PRACTICAL, PANTHER, TRAITS, INCEPT, and REMAP-CAP investigators), they provide a vision (that is increasingly becoming a reality) for experimental research in critical care.In doing so, they walk readers through the innovative design aspects and scope of each adaptive trial and take a broader look at what is needed to fully realize the potential benefits of adaptive trial designs in critical care.In a pair of Viewpoints, de Grooth and Cremer (pp.483-484) and Goligher and Harhay (pp.485-487) scrutinize the growing number of Bayesian (re)analyses of critical care trials (2, 3).Together, the authors offer new insights and perspectives on how Bayesian approaches should (and should not) be used as the field increasingly moves toward Bayesian trial interpretations after decades of disappointing clinical trial results with the more familiar P value-based framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.086 | 0.055 |
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".