Population enrichment for critical care trials: phenotypes and differential outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE OF REVIEW: Sepsis and acute respiratory distress syndrome (ARDS) are two heterogenous acute illnesses where numerous RCTs have indeterminate results. We present a narrative review on the recent developments in enriching patient populations for future sepsis and ARDS trials. RECENT FINDINGS: Many researchers are actively pursuing enrichment strategies to reduce heterogeneity to increase the sensitivity of future trials. Enrichment refers to the use of measurable patient characteristics, known before randomisation, to refine trial populations. Biomarkers could increase the diagnostic certainty of sepsis, whereas chest radiology training to enhance reliability of interpretation and stabilisation period of mechanical ventilation have been considered to increase the diagnostic certainty of ARDS. Clinical and biomarker data analyses identifies four to six sepsis clinical phenotypes and two ARDS clinical phenotypes. Similarly, leukocyte gene expression data identifies two to four sepsis molecular phenotypes. Use of a test-dose identifies ARDS subpopulations who are likely to benefit from higher PEEP. Early-phase trials report how a biomarker that is altered by the intervention, such as lymphocyte count for recombinant interleukin-7 therapy and higher check point inhibitor expression for anti-check point treatments in sepsis, could identify a higher treatment effect population for future trials. SUMMARY: Enrichment reduces heterogeneity and will enhance the sensitivity of future trials. However, enrichment, even when it identifies more homogenous populations, may not be efficient to deploy in trials or clinical practice.
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.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it