Impact of outreach education program on outcomes of neonates with hypoxic ischemic encephalopathy
Bibliographic record
Abstract
AIM: To evaluate the impact of outreach education targeting neuroprotection on outcomes of outborn infants with moderate-to-severe hypoxic ischemic encephalopathy (HIE). METHODS: A retrospective cohort study of infants admitted with moderate-to-severe HIE was conducted following the implementation of outreach education in January 2016. Key interventions were early identification and referral of infants with encephalopathy utilizing telemedicine and a centralized communication system, hands-on simulation, and interactive case discussion and dissemination of clinical management guidelines and educational resources. The association between the intervention and a composite outcome of death and/or severe brain injury on brain magnetic resonance imaging (MRI) was tested controlling for the confounding factors. RESULTS: Of 165 neonates, 37 (22.4%) died and/or had a severe brain injury. This outcome decreased from 35% (27/77) to 11% (10/88) following the implementation of outreach education (P<0.001). Eligible infants not undergoing therapeutic hypothermia within 6 hours from birth decreased from 19.5% (15/77) to 4.5% (4/88). The use of inotropes decreased from 49.3% (38/77) to 19.6% (13/88). Any core temperature below 33°C was recorded for 20/53 (38%) before and 16/78 (21%) after, while those within the target range of 33°C to 34°C at admission to a tertiary care facility increased from (15/53) 28% to (51/88) 58%. Outreach education was independently associated with decreased composite outcome of death and/or severe brain injury on MRI (adjusted odds ratio 0.2; 95% confidence interval 0.07 to 0.52). CONCLUSION: Outreach education targeting neuroprotection for infants with moderate-to-severe HIE was associated with a reduction in death and/or severe brain injury.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".