Edema description in pediatric critical care: terms, patterns, and clinical characteristics
Bibliographic record
Abstract
Abstract Background No validated methods are currently available to objectively quantify edema in critical illness. Therefore, the frequency, timing of onset, and association between edema and outcomes are unknown. Since clinicians document the presence of edema based on physical examination in the medical record, natural language processing techniques could be used to better understand its epidemiology. The objectives of this study were to describe the patterns of edema documentation by clinicians and to compare clinical characteristics and outcomes of children with and without edema. Methods An observational cohort study was conducted in a quaternary, university-affiliated pediatric intensive care unit (ICU). Eligible patients were aged 0–18 years and admitted to ICU for a minimum of 12 h. Results A total of 7884 patient admissions were studied, and a cumulative total of 211,122 notes were evaluated. Approximately, 40% of patient admissions had documented edema, of which 49% occurred before day 2 of ICU stay. The most commonly documented term across all provider types was “edema”. In multivariable regression analysis, patients who were mechanically ventilated and those with NIV, ECMO support, RRT, and vasoactive support had an increased odds of edema documentation throughout ICU stay. There was also an adjusted increased odds of death of (aOR 1.6, 95% CI of 1.1–2.2) in those with edema documented. Conclusions Edema is documented frequently and early in ICU stay. Children with edema documented were younger, required more invasive therapies, and, in addition to greater duration of intensive care stay, had higher mortality rates.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".