TNF-Alpha Serum Level as Prognostic Factor in Pediatric Sepsis Patients
Bibliographic record
Abstract
OBJECTIVE: The study aimed to investigate the role of TNFα-308 genetic polymorphism, association between TNF-α serum level and prognostic factor of mortality in pediatric sepsis. METHODS: This was a prospective cohort study. Consecutive sampling method was used and samples were obtained from septic patients diagnosed based on the IPSC 2005 criteria. Serum TNF-α and genetic polymorphism were measuread and analyzed with ELISA and PCR plus sequencing, respectively. RESULT: One hundred and seventeen samples were included, 62 were in survivor grioup and 55 in non survivor group. A very significant association was found between TNF-α serum level and mortality (p<0.001). The optimal cut off point of TNFα serum level as prognostic factor for mortality was ≥ 500 pg/mL (p<0.001 and OR 16.6) sensitivity 78.1%, specificity 82%, Positive Predictive Value (PPV) 79.6%, Negative Predictive Value (NPV) 80.9%, Area Under Curve (AUC) 0.811. Two samples showed TNFα-308A polymorphism and mutation of GG allele to heterozygote GA allele. Neither TNFα polymorphism and TNFα serum level showed any association with mortality. There was no significant association between TNFα-308 polymorphism and TNF-α serum level p=0.461(p>0.05) and mortality p=0.219 (p>0.05), all sample who had TNFα-308 genetic polymorphism were in non survivor group and had TNF-α serum level ≥ 500 pg/mL. CONCLUSION: Genetic polymorphism of TNF-α-308 showed no statistic significant on mortality, but all subjects with TNFα-308 polymorphism had higher TNF-α serum and were all in non-survivor group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".