Bibliographic record
Abstract
Informed consent plays an important role in critical care medicine (CCM) because patients are vulnerable so their rights must be carefully protected, and research in CCM may be associated with high risks of morbidity and mortality. However, the process of obtaining consent may be a significant barrier to CCM research. There is often limited time in which to make decisions, patients or substitute decision makers may not be able to make those decisions, and complex consent documents may be an additional barrier. This is an important issue, both because vulnerable patients should not be denied access to the benefits of research and because the loss of eligible patients from CCM clinical trials due to lack of consent could introduce bias and limit the generalizability of research. This article will explore why getting consent is challenging in CCM research, and how this can process could be improved.
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 | 0.505 | 0.627 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.012 | 0.061 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.044 | 0.040 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".