A Systematic Review of Risk Factors Associated With Cognitive Impairment After Pediatric Critical Illness*
Bibliographic record
Abstract
OBJECTIVES: To identify risk factors associated with cognitive impairment as assessed by neuropsychologic tests in neurotypical children after critical illness. DATA SOURCES: For this systematic review, we searched the Cochrane Library, Scopus, PubMed, Ovid, Embase, and CINAHL databases from January 1960 to March 2017. STUDY SELECTION: Included were studies with subjects 3-18 years old at the time of post PICU follow-up evaluation and use of an objective standardized neuropsychologic test with at least one cognitive functioning dimension. Excluded were studies featuring patients with a history of cardiac arrest, traumatic brain injury, or genetic anomalies associated with neurocognitive impairment. DATA EXTRACTION: Twelve studies met the sampling criteria and were rated using the Newcastle-Ottawa Quality Assessment Scale. DATA SYNTHESIS: Ten studies reported significantly lower scores in at least one cognitive domain as compared to healthy controls or normed population data; seven of these-four case-control and three prospective cohort studies-reported significant lower scores in more than one cognitive domain. Risk factors associated with post critical illness cognitive impairment included younger age at critical illness and/or older age at follow-up, low socioeconomic status, high oxygen requirements, and use of mechanical ventilation, sedation, and pain medications. CONCLUSIONS: Identifying risk factors for poor cognitive outcomes post critical illness may help healthcare teams modify patient risk and/or provide follow-up services to improve long-term cognitive outcomes in high-risk children.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".