Oral neutrophils are an independent marker of the systemic inflammatory response after cardiac bypass
Bibliographic record
Abstract
BACKGROUND: Cardiopulmonary bypass (CPB) is an immuno-reactive state where neutrophils are activated and accumulate in different tissues. Edema and tissue necrosis are the most common sequelae observed, predominantly in the lungs, kidneys, and heart, heralding significant risk for postoperative complications. No method exists to noninvasively assess in vivo neutrophil activity. The objective of this study was to determine if neutrophil recruitment to the oral cavity would correlate with specific biomarkers after coronary bypass surgery (CPB). METHODS: We conducted a single site prospective observational study including non-consecutive adult patients undergoing elective, on-pump CPB. Blood and either oral cavity rinses or swabs were collected pre- and post-CPB. Absolute neutrophil counts from oral samples and serum biomarkers were measured. The association between neutrophil recruitment to the oral cavity, biomarkers and outcomes after CPB were analyzed. RESULTS: CPB was associated with statistically significant increases in oral and blood neutrophil counts, as well as an increase in certain biomarkers over preoperative baseline. Peripheral blood neutrophil count were increased at all time points however statistically significant differences in median oral neutrophil counts were observed only at the time point immediately postoperative, and in what seems to be two unique patient populations (p < 0.001; group 1, median: 1.6×10(5), Interquartile range [IQR], 1.1×10(5) - 4.8×10(5), and group 2, median: 1.9×10(6), IQR, 8.7×10(5) - 4.0×10(6)). CONCLUSIONS: CPB is associated with a transient increase in oral neutrophils that may correlate with the systemic inflammatory response; oral neutrophils may have the ability to discriminate and identify unique patient populations based on their tissue migration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".