Effect of hyperglycemia on all-cause mortality from pediatric brain injury
Bibliographic record
Abstract
BACKGROUND: This study aimed to assess the effect of hyperglycemia on all-cause mortality in pediatric patients with brain injury, based on currently available evidence. METHODS: We systematically searched the PubMed, Embase, and Cochrane Library databases with the keywords "hyperglycemia", "brain injury", and "pediatrics". The retrieved records were screened by title, abstract, and full-text to include original articles assessing the effects of hyperglycemia on pediatric brain injury. The extracted data were assessed by a fixed-effects model. The risk of bias in the eligible studies was evaluated with the Newcastle-Ottawa Scale. Publication bias was visually examined with a funnel plot. Begg and Egger tests, respectively, were used to identify small-study effects. Sensitivity analysis was performed to evaluate the robustness of the original effect size. RESULTS: Nine observational studies were identified from 1439 primary hits. A total of 970 pediatric patients, including 304 with hyperglycemia and brain injury, were included for meta-analysis. Hyperglycemia was strongly associated with a higher risk of all-cause mortality in pediatric patients (odds ratio = 11.60, 95% confidence interval [CI] 7.88-17.08; I = 0%). The overall quality of eligible studies was low, but the funnel plot indicated no publication bias. CONCLUSIONS: Hyperglycemia is significantly associated with high all-cause mortality in pediatric patients with brain injury. However, the relationship should be confirmed by larger-scale observational studies and randomized controlled trials.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".