Role of Neutrophil CD64 in the Diagnosis of Neonatal Sepsis
Bibliographic record
Abstract
Introduction: Neutrophil surface CD64 (Cluster of differentiation 64), the highaffinity Fc receptor, is quantitatively up-regulated during infection and sepsis. The diagnostic utility of NCD64 as a reliable marker of neonatal sepsis has not been explored so far. Hence this study has been conducted to compare NCD64 with other currently used infection markers including total leucocyte count, platelet count, absolute neutrophil count (ANC), band:neutrophil ratio and highly sensitive C reactive protein (hs-CRP). Methods: Consecutively born neonates between March 2014 to November 2014 were enrolled with documented sepsis (n = 81), clinical sepsis (n = 35), and no sepsis (n = 87). NCD64 was analyzed by flow cytometry. Results: Sepsis episodes had a higher median CD64 index of 10.35 (Range: 15.88, 6.87) as against 2.97 (Range: 5.53, 1.64) in the control group (p < 0.001). The percentage of NCD64 positive cells was also significantly higher in the sepsis group compared to the control group (63.90 ± 2.67 vs 15.07 ± 1.95; p = 0.001). In the ROC curve analysis NCD64, percentage of NCD64 positive cells had the highest AUC (AUC-0.914) using a cutoff of 28.01%, followed by CD64 mean fluorescence intensity (MFI) with an AUC of 0.850 using a cutoff of 5.54. NCD64 was significantly elevated in the groups with documented and clinical sepsis (p < 0.001). Conclusions: NCD64 is a highly sensitive marker for neonatal sepsis. Prospective studies incorporating NCD64 into a sepsis scoring system are warranted.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".