Abstract P-253: SALIVARY MELATONIN DETERMINATION HAS POOR FEASIBILITY IN CRITICALLY ILL CHILDREN
Bibliographic record
Abstract
Aims & Objectives: Melatonin secretion patterns in critically ill children are poorly understood. We performed this study to examine the feasibility of salivary melatonin determination in critically ill children, defining feasibility as successful sample acquisition and analysis on >75% of children tested. Methods Prospective, observational study of children admitted to the PICU who had access for blood work and PRISM III of ≥1. Salivary (1mL) and serum (2mL) samples were collected at 3am; saliva by spitting or sublingual suction, with oral care restricted for 30 minutes prior. Samples were stored at -80oC, and were determined in duplicate by direct ELISA. Results Salivary samples were attempted in 46 patients with median age of 84.0 (IQR = 148.4) months, and median PRISM III of 10 (IQR=8). Sample was unobtainable in 15 (33%) patients because of insufficient saliva (n=12), no cooperation (n=2), or bloody/mucousy sample (n=1). Comparing children with and without a sample, there was no difference in median age (p=0.38), PRISM III (p=0.94), or proportion who were mechanically ventilated (25/31 versus 10/15 respectively, p=0.46). Of the samples taken, 13 salivary melatonin levels were out of the detection range of the commercially-available ELISA kit (11 too high, 2 too low). For the remaining 18, median salivary melatonin was 19.4pg/mL (IQR=27.5pg/mL). Correlation with serum melatonin was non-significant (n=8, median 19.3 [IQR=35.6]pg/mL, Pearson’s r = 0.57, p=0.14). Conclusions Saliva samples are difficult to obtain in critically ill children. Salivary melatonin determination has poor feasibility in critically ill children, and correlation with serum melatonin requires further investigation.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".