Metabolomic and inflammatory mediator based biomarker profiling as a potential novel method to aid pediatric appendicitis identification
Bibliographic record
Abstract
Various limitations hinder the timely and accurate diagnosis of appendicitis in pediatric patients. The present study aims to investigate the potential of metabolomics and cytokine profiling for improving the diagnosis of pediatric appendicitis. Serum and plasma samples were collected from pediatric patients for metabolic and inflammatory mediator analyses respectively. Targeted metabolic profiling was performed using Proton Nuclear Magnetic Resonance Spectroscopy and Flow Injection Analysis Mass Spectrometry/Mass Spectrometry and targeted cytokine/chemokine profiling was completed using a multiplex platform to compare children with and without appendicitis. Twenty-three children with appendicitis and 35 control children without appendicitis from the Alberta Sepsis Network pediatric cohorts were included. Metabolomic profiling revealed clear separation between the two groups with very good sensitivity (80%), specificity (97%), and AUROC (0.93 ± 0.05) values. Inflammatory mediator analysis also distinguished the two groups with high sensitivity (82%), specificity (100%), and AUROC (0.97 ± 0.02) values. A biopattern comprised of 9 metabolites and 7 inflammatory compounds was detected to be significant for the separation between appendicitis and control groups. Integration of these 16 significant compounds resulted in a combined metabolic and cytokine profile that also demonstrated strong separation between the two groups with 81% sensitivity, 100% specificity and AUROC value of 0.96 ± 0.03. The study demonstrated that metabolomics and cytokine mediator profiling is capable of distinguishing children with appendicitis from those without. These results suggest a potential new approach for improving the identification of appendicitis in children.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".