Diagnostic Utility of Different Blood Components in Gene Expression Analysis of Sepsis
Bibliographic record
Abstract
RATIONALE: Most gene expression studies of sepsis have used either whole blood or specific leukocyte fractions as source tissues for RNA. Data regarding the relative utility of these different tissue sources are lacking. OBJECTIVES: To evaluate the utility of different source tissues in studying gene expression in sepsis. METHODS: We undertook a systematic analysis of sepsis gene expression studies, including both adult and pediatric cohorts. We used clustering methods to partition samples according to gene expression levels, and compared expression cluster labels to clinical diagnoses. We also quantified the strength of cluster formation based on expression data from different tissue sources using average silhouette widths as a measure of cluster cohesiveness. RESULTS: We included 22 separate expression datasets. Whole blood was used as the source tissue in 15 studies, while leukocyte isolates were used in seven studies. Whole blood samples yielded greater specificity for the diagnosis of sepsis than data from leukocyte isolates (94% vs 78%, P = 0.03). Whole blood-derived data also yielded more cohesive clusters (median silhouette widths 0.28 and 0.19 for whole blood and leukocyte isolates respectively, P < 0.01). CONCLUSION: Our results support the use of whole blood to derive gene expression data in sepsis studies investigating novel diagnostics and subtype discovery. This strategy has a number of practical advantages, and the resulting data also have potential utility in developing molecular classifications of sepsis syndromes.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".