Severity and duration of dysglycemia and brain injury among patients with neonatal encephalopathy
Bibliographic record
Abstract
Background: Evidence is needed to inform thresholds for glycemic management in neonatal encephalopathy (NE). We investigated how severity and duration of dysglycemia relate to brain injury after NE. Methods: A prospective cohort of 108 neonates ≥36 weeks gestational age with NE were enrolled between August 2014 and November 2019 at the Hospital for Sick Children, in Toronto, Canada. Participants underwent continuous glucose monitoring for 72 h, MRI at day 4 of life, and follow-up at 18 months. Receiver operating characteristic curves were used to assess the predictive value of glucose measures (minimum and maximum glucose, sequential 1 mmol/L glucose thresholds) during the first 72 h of life (HOL) for each brain injury pattern (basal ganglia, watershed, focal infarct, posterior-predominant). Linear and logistic regression analyses were used to assess the relationship between abnormal glycemia and 18-month outcomes (Bayley-III composite scores, Child Behavior Checklist [CBCL] T-scores, neuromotor score, cerebral palsy [CP], death), adjusting for brain injury severity. Findings: = 0.801). All associations with outcome lost significance after adjusting for brain injury severity. Interpretation: Maximum glucose concentration in the first 48 HOL is predictive of brain injury after NE. Further trials are needed to assess if protocols to control maximum glucose concentrations improve outcomes after NE. Funding: Canadian Institutes for Health Research, National Institutes of Health, and SickKids Foundation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".