Evaluating small vessel neutrophils as a marker for sepsis
Bibliographic record
Abstract
A retrospective case-control study of 100 sepsis autopsy cases and 103 controls over a 9-year period was conducted to analyze patterns of neutrophils in small caliber vessels of the liver, heart, and lungs in relation to sepsis as the cause of death. Data extracted included demographics of the decedent, cause of death, presence of conditions that could interfere with an inflammatory response, history of hospitalization, and results of microbiology cultures. Histologic sections of the liver, heart, and lungs were assessed. Organs were scored for neutrophilic inflammation based upon a predetermined grading system. Scores of 0, 1, and 2 were assigned according to mild, moderate, and florid neutrophilic presence, respectively; a total score was also assigned based on the sum of the scores from all three organs. Comparing the histologic grading between cases and controls found a statistical difference with the neutrophil grading in the liver (p < 0.001), lung (p < 0.001), and heart (p < 0.001) and between the combined total scores (p < 0.001). Combined neutrophilic scores of 4 and greater showed high specificities (90% to 100%) for sepsis-related deaths. Examining the percentage of sepsis cases as the histologic neutrophilic score increased found a positive slope in all three organs. However, only the linear regression looking at the lung (p = 0.03) and the combined score (p = 0.001) were statistically significant. Despite the above results, sepsis cases with low scores and controls with moderate and florid neutrophilic infiltrates were also seen.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".