Mean Platelet Volume in Preterm: A Predictor of Early Onset Neonatal Sepsis
Bibliographic record
Abstract
Background: Early onset sepsis (EOS) is potentially life-threatening problem especially in preterm. EOS diagnosis is challenging due to its non-specific signs and laboratory tests. Mean platelet volume (MPV) has been used as predictor of many inflammatory diseases. Objectives: To assess the correlation between serial MPV measurement and EOS occurrence in preterm and to determine MPV effectiveness in combination with CRP to diagnose EOS and mortality prediction. Methods: The study was carried out on 95 preterms with antenatal risk factor for EOS. Blood samples were taken for complete blood count (CBC) including MPV evaluated at birth (cord blood) and at 72 hours of life. CRP analyzed on day 1 and 3, subsequently patients were identified in 2 groups: sepsis (n = 28) and non-sepsis. (n = 67). Results: MPV was significantly higher on both day 1(10.23±0.92) fl and day 3(10.77±1.16) fL in sepsis group compared to non-sepsis (8.11±0.29) fl and (8.53±0.42) fl, respectively. MPV of 8.6 fL was identified as cut off value in patients probably resulting in sepsis with sensitivity of 97.14% and specificity of 100%. MPV of 10.4 fL was determined as cut off value in patients possibly resulting in death with sensitivity of 70% and specificity of 82.5%. The combination of both MPV and CRP on day 1 resulted in improving performance of MPV with higher negative predictive value (93.1%) and higher sensitivity (80%).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".