Does Persistent Inflammatory Catabolic Syndrome Exist in Critically Ill Neonates?
Bibliographic record
Abstract
BACKGROUND: Persistent inflammatory catabolic syndrome (PICS) has not been described in the infant population. This study proposes a definition of PICS in critically ill infants. METHODS: A published adult criterion of PICS was modified using anthropometric and biochemical reference ranges for infants. A prospective chart review of admissions to a tertiary surgical neonatal intensive care unit (NICU) was performed over 65 days. Demographic, anthropometric, biochemical, and other clinical variables such as length of stay and medication use were collected daily throughout admission. Infants were categorized as having or not having PICS. RESULTS: Twenty percent of admitted infants (n = 15) developed PICS using the proposed criteria. Infants with PICS were more likely to be classified as failure to thrive (53%), meeting only 75% of their anticipated weight gain. Significantly more infants with PICS had undergone surgery (100%; P = .01), received inotropic medication (40%; P = .05), and had longer NICU and total hospital length of stay ( P < .001 and P < .001). Infants with PICS had higher peak glucose levels (11.8 ± 7.3 mmol/L) and elevated urea concentrations (7.9 ± 4.6 mmol/L). CONCLUSIONS: PICS does exist in a critically ill neonatal population and may be identified using the definition proposed in this study. Infants with PICS displayed metabolic dysregulation, impaired expected growth velocity, and longer length of stay despite no differences in severity scores or diagnosis between the groups. Validation of this work is required, and research into timely identification of infants with PICS is needed to inform whether these infants would benefit from earlier and novel nutrition intervention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".