Bibliographic record
Abstract
In the previous issue of Critical Care, Tang and colleagues offer a very novel systematic review of 12 studies of gene expression in blood of human sepsis. The review concludes that there is no discernable transition from a pro- to an anti-inflammatory expression phenotype in blood in human sepsis. The authors found that upregulation of pathogen recognition receptors and signal transduction pathways was a consistent theme in expression studies. The review by Tang and colleagues has strengths, including defined screening criteria, broad literature review, strict inclusion criteria, and transparent methods for assessing strengths and weaknesses of studies. There are other issues to consider. First, one source of variation in gene expression studies in sepsis is variability in time from onset of sepsis to time of blood draw. Another source of variation is differences between tissues in gene expression at the same time in sepsis. Whole blood is a mélange of tissues (a variety of leukocytes); therefore, one assesses a weighted sum of all leukocyte classes. About half of the studies assessed peripheral mononuclear cells. A third great source of variable gene expression in sepsis is heterogeneity of causes and microbiology of sepsis. Only one study compared Gram-positive with Gram-negative sepsis. Only three studies confirmed microarray data with an independent measurement of expression. One interpretation is that two of three studies of early sepsis found increased expression of pro-inflammatory genes. In late sepsis, three of six studies found increased expression of pro-inflammatory genes whereas three of six studies found decreased expression of pro-inflammatory genes. The balance of pro- to anti-inflammatory gene expression is difficult to quantify. Sample size is highly variable in studies (n = 12 to 176). These limitations require a leap of faith to suggest that the paradigm of sepsis as a pro-inflammatory phenotype that shifts to an anti-inflammatory phenotype is flawed: the absence of evidence in expression studies is not the same as having well-conducted studies with clear negative evidence.
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 | 0.027 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".