Early fluid overload was associated with prolonged mechanical ventilation and more aggressive parameters in critically ill paediatric patients
Bibliographic record
Abstract
AIM: We evaluated the influence of early fluid overload on critically ill children admitted to a paediatric intensive care unit by examining mechanical ventilation (MV), mortality, length of stay and renal replacement therapy. METHODS: This retrospective cohort study covered January 2015 to December 2016 and focused on all episodes of MV support that exceeded 24 hours. The fluid overload percentage (FO%) was calculated daily for the first 72 hours and we estimated its effect on outcomes. RESULTS: We included 186 MV episodes in 154 patients. The median age was 13.8 months, with an interquartile range (IQR) of 3.8-34.0 months, and the mortality rate was 12.4%. The median FO% in the first 72 hours was 8.0% (IQR 3.6%-11.2%). An FO% of ≥10% was associated with higher ventilatory parameters, namely peak inspiratory pressure (P = .023) and positive end expiratory pressure (P = .003), and renal replacement therapy (P = .02) and higher mortality (8.8% vs 19.7%). In a multivariate Cox regression model, FO ≥ 10% at 72 hours was independently associated with longer MV support, but not mortality (P = .001). CONCLUSION: In a heterogeneous paediatric population given MV, an early cumulative FO of ≥10% was associated with more aggressive ventilatory parameters and prolonged length of MV, but not mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".